1. The Prompt Report: A Systematic Survey of Prompting Techniques Sander Schulhoff 1,2∗ Michael Ilie 1∗ Nishant Balepur 1 Konstantine Kahadze 1 Amanda Liu 1 Chenglei Si 4 Yinheng Li 5 Aayush Gupta 1 HyoJung Han 1 Sevien Schulhoff 1 Pranav Sandeep Dulepet 1 Saurav Vidyadhara 1 Dayeon Ki 1 Sweta Agrawal 12 Chau Pham 13 Gerson Kroiz Feileen Li 1 Hudson Tao 1 Ashay Srivastava 1 Hevander Da Costa 1 Saloni Gupta 1 Megan L. Rogers 8 Inna Goncearenco 9 Giuseppe Sarli 9,10 Igor Galynker 11 7 Denis Peskoff Marine Carpuat 1 Jules White 6 Shyamal Anadkat 3 Alexander Hoyle 1 Philip Resnik 1 1 3 University of Maryland 2 Learn Prompting OpenAI 4 Stanford 5 Microsoft 6 Vanderbilt 7 Princeton 8 9 Texas State University Icahn School of Medicine 10 ASST Brianza 11 Mount Sinai Beth Israel 12 Instituto de Telecomunicações 13 University of Massachusetts Amherst sschulho@umd.edu milie@umd.edu resnik@umd.edu Abstract Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area’s nascency. This paper establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. We present a comprehensive vocabulary of 33 vocabulary terms, a taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities. We further present a meta-analysis of the entire literature on natural language prefix-prompting. 1

2. Contents 1 . . . . . 4 5 5 5 6 7 2 A Meta-Analysis of Prompting 2.1 Systematic Review Process . . . . 2.1.1 The Pipeline . . . . . . . 2.2 Text-Based Techniques . . . . . . 2.2.1 In-Context Learning (ICL) 2.2.2 Zero-Shot . . . . . . . . . 2.2.3 Thought Generation . . . 2.2.4 Decomposition . . . . . . 2.2.5 Ensembling . . . . . . . . 2.2.6 Self-Criticism . . . . . . . 2.3 Prompting Technique Usage . . . 2.3.1 Benchmarks . . . . . . . . 2.4 Prompt Engineering . . . . . . . . 2.5 Answer Engineering . . . . . . . 2.5.1 Answer Shape . . . . . . 2.5.2 Answer Space . . . . . . . 2.5.3 Answer Extractor . . . . . 8 8 8 8 8 11 12 13 14 15 15 17 17 17 18 18 18 3 Beyond English Text Prompting 3.1 Multilingual . . . . . . . . . . . . 3.1.1 Chain-of-Thought (CoT) . 3.1.2 In-Context Learning . . . 3.1.3 In-Context Example Selec- tion . . . . . . . . . . . . 3.1.4 Prompt Template Lan- guage Selection . . . . . . 3.1.5 Prompting for Machine Translation . . . . . . . . 3.2 Multimodal . . . . . . . . . . . . 3.2.1 Image Prompting . . . . . 3.2.2 Audio Prompting . . . . . 3.2.3 Video Prompting . . . . . 3.2.4 Segmentation Prompting . 3.2.5 3D Prompting . . . . . . . 19 19 19 19 Extensions of Prompting 4.1 Agents . . . . . . . . . . . . . . . 4.1.1 Tool Use Agents . . . . . 23 23 23 4 Introduction 1.1 What is a Prompt? . . . . . . . . 1.2 Terminology . . . . . . . . . . . 1.2.1 Components of a Prompt 1.2.2 Prompting Terms . . . . 1.3 A Short History of Prompts . . . 4.1.2 4.1.3 4.1.4 4.2 5 6 Prompting Issues 5.1 Security . . . . . . . . . . . . . . 5.1.1 Types of Prompt Hacking . 5.1.2 Risks of Prompt Hacking . 5.1.3 Hardening Measures . . . 5.2 Alignment . . . . . . . . . . . . . 5.2.1 Prompt Sensitivity . . . . 5.2.2 Overconfidence and Cali- bration . . . . . . . . . . 5.2.3 Biases, Stereotypes, and Culture . . . . . . . . . . 5.2.4 Ambiguity . . . . . . . . 23 24 24 25 25 26 26 26 28 28 28 28 29 29 30 30 31 31 Benchmarking 6.1 Technique Benchmarking . . . . . 6.1.1 Comparing Prompting Techniques . . . . . . . . 6.1.2 Question Formats . . . . . 6.1.3 Self-Consistency . . . . . 6.1.4 Evaluating Responses . . 6.1.5 Results . . . . . . . . . . 6.2 Prompt Engineering Case Study . 6.2.1 Problem . . . . . . . . . . 6.2.2 The Dataset . . . . . . . . 6.2.3 The Process . . . . . . . . 6.2.4 Discussion . . . . . . . . 32 32 7 Related Work 42 8 Conclusions 43 19 19 20 21 21 22 22 22 22 2 Code-Generation Agents . Observation-Based Agents Retrieval Augmented Gen- eration (RAG) . . . . . . Evaluation . . . . . . . . . . . . . 4.2.1 Prompting Techniques . . 4.2.2 Output Format . . . . . . 4.2.3 Prompting Frameworks . . 4.2.4 Other Methodologies . . . 32 32 32 33 33 33 33 34 34 41

3. A Appendices A.1 Definitions of Prompting . . . . . A.2 Extended Vocabulary . . . . . . . A.2.1 Prompting Terms . . . . . A.2.2 Prompt Engineering Terms A.2.3 Fine-Tuning Terms . . . . A.2.4 Orthogonal Prompt Types A.3 Datasheet . . . . . . . . . . . . . A.3.1 Motivation . . . . . . . . A.3.2 Composition . . . . . . . A.3.3 Collection Process . . . . A.3.4 Preprocessing/ Cleaning/ Labeling . . . . . . . . . A.3.5 Uses . . . . . . . . . . . . 59 59 61 61 61 61 61 63 63 63 64 A.4 A.5 A.6 A.7 A.8 64 64 A.9 3 A.3.6 Distribution . . . . . . . . A.3.7 Maintenance . . . . . . . Keywords . . . . . . . . . . . . . Evaluation Table . . . . . . . . . Entrapment Prompting Process . . A.6.1 Exploration . . . . . . . . A.6.2 Getting a Label . . . . . . A.6.3 Varying Prompting Tech- niques . . . . . . . . . . . Formally Defining a Prompt . . . In-Context Learning Definitions Disambiguation . . . . . . . . . . Contributions . . . . . . . . . . . 64 64 65 67 68 68 68 68 71 73 75

4. 1 Introduction Transformer-based LLMs are widely deployed in consumer-facing, internal, and research settings (Bommasani et al., 2021). Typically, these models rely on the user providing an input “prompt” to which the model produces an output in response. Such prompts may be textual—“Write a poem about trees.”—or take other forms: images, audio, videos, or a combination thereof. The ability to prompt models, particularly prompting with natu- ral language, makes them easy to interact with and use flexibly across a wide range of use cases. Core Prompting Techniques Text based Techniques MLT/MMTs are often derived from fundamental text-based prompting techniques. Multilingual Techniques *Techniques on text data from multiple languages Multimodal Techniques *Techniques for processing multimedia (video, audio, etc) Agents Often make use of core prompting techniques Knowing how to effectively structure, evaluate, and perform other tasks with prompts is essential to using these models. Empirically, better prompts lead to improved results across a wide range of tasks (Wei et al., 2022; Liu et al., 2023b; Schul- hoff, 2022). A large body of literature has grown around the use of prompting to improve results and the number of prompting techniques is rapidly increasing. Safety Safety needs throughout Evaluation Need to evaluate prompt/ agent outputs Security Security concerns throughout Figure 1.1: Categories within the field of prompting are interconnected. We discuss 7 core categories that are well described by the papers within our scope. work approachable to less technical readers and maintain a manageable scope. However, as prompting is an emerging field, the use of prompts continues to be poorly understood, with only a fraction of existing terminologies and techniques being well-known among practitioners. We perform a large-scale review of prompting tech- niques to create a robust resource of terminology and techniques in the field. We expect this to be the first iteration of terminologies that will develop over time. Sections Overview We conducted a machine- assisted systematic review grounded in the PRISMA process (Page et al., 2021) (Section 2.1) to identify 58 different text-based prompting tech- niques, from which we create a taxonomy with a robust terminology of prompting terms (Section 1.2). While much literature on prompting focuses on English-only settings, we also discuss multilingual techniques (Section 3.1). Given the rapid growth in multimodal prompting, where prompts may include media such as images, we also expand our scope to multimodal techniques (Section 3.2). Many mul- tilingual and multimodal prompting techniques are direct extensions of English text-only prompting techniques. As prompting techniques grow more complex, they have begun to incorporate external tools, such as Internet browsing and calculators. We use the term ‘agents‘ to describe these types of prompting techniques (Section 4.1). It is important to understand how to evaluate the outputs of agents and prompting techniques to ensure accuracy and avoid hallucinations. Thus, we discuss ways of evaluating these outputs (Sec- Scope of Study We create a broad directory of prompting techniques, which can be quickly un- derstood and easily implemented for rapid experi- mentation by developers and researchers. To this end, we limit our study to focus on discrete pre- fix prompts (Shin et al., 2020a) rather than cloze prompts (Petroni et al., 2019; Cui et al., 2021), because modern LLM architectures (especially decoder-only models), which use prefix prompts, are widely used and have robust support for both consumers and researchers. Additionally, we re- fined our focus to hard (discrete) prompts rather than soft (continuous) prompts and leave out papers that make use of techniques using gradient-based updates (i.e. fine-tuning). Finally, we only study task-agnostic techniques. These decisions keep the 4

5. tion 4.2). We also discuss security (Section 5.1) and safety measures (Section 5.2) for designing prompts that reduce the risk of harm to companies and users. Write a poem about trees. Write a poem about the following topic: {USER_INPUT} Finally, we apply prompting techniques in two case studies (Section 6.1). In the first, we test a range of prompting techniques against the com- monly used benchmark MMLU (Hendrycks et al., 2021). In the second, we explore in detail an exam- ple of manual prompt engineering on a significant, real-world use case, identifying signals of frantic hopelessness–a top indicator of suicidal crisis–in the text of individuals seeking support (Schuck et al., 2019a). We conclude with a discussion of the nature of prompting and its recent development (Section 8). 1.1 Figure 1.2: Prompts and prompt templates are distinct concepts; a prompt template becomes a prompt when input is inserted into it. 1.2 Terminology 1.2.1 Components of a Prompt There are a variety of common components in- cluded in a prompt. We summarize the most com- monly used components and discuss how they fit into prompts. What is a Prompt? Directive Many prompts issue a directive in the form of an instruction or question. 1 This is the core intent of the prompt, sometimes simply called the "intent". For example, here is an example of a prompt with a single instruction: A prompt is an input to a Generative AI model, that is used to guide its output (Meskó, 2023; White et al., 2023; Heston and Khun, 2023; Hadi et al., 2023; Brown et al., 2020). Prompts may consist of text, image, sound, or other media. Some examples of prompts include: “write a three paragraph email for a marketing campaign for an accounting firm”, a photograph of a table accompanied by the text “describe everything on the table”, or a recording of an online meeting, with the instructions “sum- marize this”. Tell me five good books to read. Directives can also be implicit, as in this one- shot case, where the directive is to perform English to Spanish translation: Night: Noche Morning: Prompt Template Prompts are often constructed via a prompt template (Shin et al., 2020b). A prompt template is a function that contains one or more variables which will be replaced by some me- dia (usually text) to create a prompt. This prompt can then be considered to be an instance of the template. Examples Examples, also known as exemplars or shots, act as demonstrations that guide the GenAI to accomplish a task. The above prompt is a One- Shot (i.e. one example) prompt. Output Formatting It is often desirable for the GenAI to output information in certain formats, for example, CSVs or markdown formats (Xia et al., 2024). To facilitate this, you can simply add in- structions to do so as seen below: Consider applying prompting to the task of bi- nary classification of tweets. Here is an initial prompt template that can be used to classify inputs. Classify the tweet as positive or negative: {TWEET} {PARAGRAPH} Summarize this into a CSV. Each tweet in the dataset would be inserted into a separate instance of the template and the resulting prompt would be given to a LLM for inference. 1 “Directives”, from Searle (1969), are a type of speech act intended to encourage an action, and have been invoked in models of human-computer dialogue Morelli et al. (1991). 5

6. Context 1.2.1 Context Window A.2.1 Priming A.2.1 Prompting Technique 1.2.2 In-Context Learning 2.2.1 Few-Shot Prompt 2.2.1 Zero-Shot Prompt 2.2.2 Continuous Prompt A.2.4.2 Prompting 1.2.2 Density A.2.4.2 Exemplar 1.2.2 Discrete Prompt A.2.4.2 User Prompt A.2.4.1 Orthogonal Prompt Types A.2.4 Originator A.2.4.1 Assistant Prompt A.2.4.1 Prompt Chain 1.2.2 Prefix A.2.4.3 Prompt Engineering Technique 1.2.2 Prompt 1.1 System Prompt A.2.4.1 Prediction Style A.2.4.3 Cloze A.2.4.3 Prompt Template 1.1 Meta-Prompting 2.4 Prompt Engineering 1.2.2 Answer Engineering 2.5 Verbalizer 2.5.3 Extractor 2.5.3 Conversational Prompt Engineering A.2.2 Fine-Tuning A.2.3 Prompt-Based Learning A.2.3 Prompt Tuning A.2.3 Figure 1.3: A Terminology of prompting. Terms with links to the appendix are not sufficiently critical to describe in the main paper, but are important to the field of prompting. Prompting techniques are shown in Figure 2.2 . 1.2.2 Style Instructions Style instructions are a type of output formatting used to modify the output stylistically rather than structurally (Section 2.2.2). For example: Prompting Terms Terminology within the prompting literature is rapidly developing. As it stands, there are many poorly understood definitions (e.g. prompt, prompt engineering) and conflicting ones (e.g. role prompt vs persona prompt). The lack of a consistent vocab- ulary hampers the community’s ability to clearly describe the various prompting techniques in use. We provide a robust vocabulary of terms used in the prompting community (Figure 1.3). 3 Less frequent terms are left to Appendix A.2. In order to accu- rately define frequently-used terms like prompt and prompt engineering, we integrate many definitions (Appendix A.1) to derive representative definitions. Write a clear and curt paragraph about lla- mas. Role A Role, also known as a persona (Schmidt et al., 2023; Wang et al., 2023l), is a frequently discussed component that can improve writing and style text (Section 2.2.2). For example: Pretend you are a shepherd and write a lim- erick about llamas. Prompting Prompting is the process of provid- ing a prompt to a GenAI, which then generates a response. For example, the action of sending a chunk of text or uploading an image constitutes prompting. Additional Information It is often necessary to include additional information in the prompt. For example, if the directive is to write an email, you might include information such as your name and position so the GenAI can properly sign the email. Additional Information is sometimes called ‘con- text‘, though we discourage the use of this term as it is overloaded with other meanings in the prompt- ing space 2 . Prompt Chain A prompt chain (activity: prompt chaining) consists of two or more prompt templates used in succession. The output of the prompt gen- erated by the first prompt template is used to pa- rameterize the second template, continuing until all templates are exhausted (Wu et al., 2022). 2 e.g. the context is the tokens processed by the LLM in a forward pass 3 By robust, we mean that it covers most existing commonly used terms in the field. 6

7. responses originated before the GPT-3 and Chat- GPT era. GPT-2 (Radford et al., 2019a) makes use of prompts and they appear to be first used in the context of Generative AI by Fan et al. (2018). However, the concept of prompts was preceded by related concepts such as control codes (Pfaff, 1979; Poplack, 1980; Keskar et al., 2019) and writing prompts. The term Prompt Engineering appears to have come into existence more recently from Radford et al. (2021) then slightly later from Reynolds and McDonell (2021). However, various papers perform prompt engi- neering without naming the term (Wallace et al., 2019; Shin et al., 2020a), including Schick and Schütze (2020a,b); Gao et al. (2021) for non- autoregressive language models. Some of the first works on prompting define a prompt slightly differently to how it is currently used. For example, consider the following prompt from Brown et al. (2020): Dataset Inference (i.e. entries x₁ ... xₙ) x₁ x₂ xₙ Prompt Template Generative AI Modify Prompt Template until Desiderata Met Extractor Utility Function Figure 1.4: The Prompt Engineering Process consists of three repeated steps 1) performing inference on a dataset 2) evaluating performance and 3) modifying the prompt template. Note that the extractor is used to extract a final response from the LLM output (e.g. "This phrase is positive" → "positive"). See more information on extractors in Section 2.5. Translate English to French: llama Prompting Technique A prompting technique is a blueprint that describes how to structure a prompt, prompts, or dynamic sequencing of multi- ple prompts. A prompting technique may incorpo- rate conditional or branching logic, parallelism, or other architectural considerations spanning multi- ple prompts. Brown et al. (2020) consider the word "llama" to be the prompt, while "Translate English to French:" is the "task description". More recent papers, in- cluding this one, refer to the entire string passed to the LLM as the prompt. Prompt Engineering Prompt engineering is the iterative process of developing a prompt by modify- ing or changing the prompting technique that you are using (Figure 1.4). Prompt Engineering Technique A prompt engi- neering technique is a strategy for iterating on a prompt to improve it. In literature, this will often be automated techniques (Deng et al., 2022), but in consumer settings, users often perform prompt engineering manually. Exemplar Exemplars are examples of a task be- ing completed that are shown to a model in a prompt (Brown et al., 2020). 1.3 A Short History of Prompts The idea of using natural language prefixes, or prompts, to elicit language model behaviors and 7

8. 2 A Meta-Analysis of Prompting 2.1 Systematic Review Process 3,677 from arXiv 2,087 from SS, 639 from ACL = 4797 Records In order to robustly collect a dataset of sources for this paper, we ran a systematic literature re- view grounded in the PRISMA process (Page et al., 2021) (Figure 2.1). We host this dataset on Hug- gingFace and present a datasheet (Gebru et al., 2021) for the dataset in Appendix A.3. Our main data sources were arXiv, Semantic Scholar, and ACL. We query these databases with a list of 44 keywords narrowly related to prompting and prompt engineering (Appendix A.4). 2.1.1 -550 -316 316 papers excluded 2,352 Records after checking for word “prompt” in paper body 1,071 papers AI reviewed -787 787 papers excluded After The PRISMA Review Process, 1,565 records included in quantitative analysis. Figure 2.1: The PRISMA review process. We accumu- late 4,247 unique records from which we extract 1,565 relevant records. 1. Does the paper propose a novel prompting technique? (include) some of the techniques might fit into multiple cate- gories, we place them in a single category of most relevance. 2. Does the paper strictly cover hard prefix prompts? (include) 2.2.1 3. Does the paper focus on training by backprop- agating gradients? (exclude) In-Context Learning (ICL) ICL refers to the ability of GenAIs to learn skills and tasks by providing them with exemplars and or relevant instructions within the prompt, without the need for weight updates/retraining (Brown et al., 2020; Radford et al., 2019b). These skills can be learned from exemplars (Figure 2.4) and/or instruc- tions (Figure 2.5). Note that the word ’learn’ is misleading. ICL can simply be task specification– the skills are not necessarily new, and can have already been included in the training data (Figure 2.6). See Appendix A.8 for a discussion of the use of this term. Significant work is currently being done on optimizing (Bansal et al., 2023) and un- derstanding (Si et al., 2023a; Štefánik and Kadlčík, 2023) ICL. 4. For non-text modalities, does it use a masked frame and/or window? (include) A set of 300 articles are reviewed independently by two annotators, with 92% agreement (Krippen- dorff’s α = Cohen’s κ = 81%). Next, we develop a prompt using GPT-4-1106-preview to classify the remaining articles. We validate the prompt against 100 ground-truth annotations, achieving 89% pre- cision and 75% recall (for an F 1 of 81%). The combined human and LLM annotations generate a final set of 1,565 papers. Text-Based Techniques We now present a comprehensive taxonomical on- tology of 58 text-based prompting techniques, bro- ken into 6 major categories (Figure 2.2). Although 4 1,661 papers human reviewed The Pipeline In this section, we introduce our data scraping pipeline, which includes both human and LLM- assisted review. 4 As an initial sample to estab- lish filtering critera, we retrieve papers from arXiv based on a simple set of keywords and boolean rules (A.4). Then, human annotators label a sample of 1,661 articles from the arXiv set for the follow- ing criteria: 2.2 4,247 Records after Title Deduplication Few-Shot Prompting (Brown et al., 2020) is the paradigm seen in Figure 2.4, where the GenAI learns to complete a task with only a few exam- ples (exemplars). Using GPT-4-1106-preview 8

9. Emotion Prompting 2.2.2 Role Prompting 2.2.2 Style Prompting 2.2.2 S2A 2.2.2 Zero-Shot 2.2.2 SimToM 2.2.2 RaR 2.2.2 RE2 2.2.2 Self-Ask 2.2.2 Exemplar Generation Few-Shot 2.2.1 SG-ICL 2.2.1.2 Exemplar Ordering 2.2.1.1 Exemplar Selection 2.2.1.2 KNN 2.2.1.2 Vote-K 2.2.1.2 Zero-Shot CoT 2.2.3.1 Analogical Prompting 2.2.3.1 Step-Back Prompting 2.2.3.1 Thread-of-Thought (ThoT) 2.2.3.1 Tab-CoT 2.2.3.1 Thought Generation 2.2.3 Chain-of-Thought (CoT) 2.2.3 Active-Prompt 2.2.3.2 Auto-CoT 2.2.3.2 COSP 2.2.5 Complexity-Based 2.2.3.2 DENSE 2.2.5 Contrastive 2.2.3.2 DiVeRSe 2.2.5 Few-Shot CoT 2.2.3.2 Max Mutual Information 2.2.5 Text-Base Prompt. Tech. Memory-of-Thought 2.2.3.2 Meta-CoT 2.2.5 Uncertainty-Routed CoT 2.2.3.2 MoRE 2.2.5 Prompt Mining 2.2.1.2 Ensembling 2.2.5 Self-Consistency 2.2.5 Universal Self-Consistency 2.2.5 USP 2.2.5 Prompt Paraphrasing 2.2.5 Chain-of-Verification 2.2.6 Self-Calibration 2.2.6 Self-Refine 2.2.6 Self-Criticism 2.2.6 Self-Verification 2.2.6 ReverseCoT 2.2.6 Cumulative Reason. 2.2.6 DECOMP 2.2.4 Faithful CoT 2.2.4 Least-to-Most 2.2.4 Plan-and-Solve 2.2.4 Decomposition 2.2.4 Program-of-Thought 2.2.4 Recurs.-of-Thought 2.2.4 Skeleton-of-Thought 2.2.4 Tree-of-Thought 2.2.4 Figure 2.2: All text-based prompting techniques from our dataset. 9

10. Exemplar Quantity Include as many exemplars as possible* Exemplar Ordering Randomly order exemplars* Exemplar Label Quality Ensure exemplars are labeled correctly* Exemplar Label Distribution Provide a balanced label distribution* Exemplar Format Choose a common format* Exemplars Similarity Select similar exemplars to the test instance* Trees are beautiful: Positive I hate Pi zz a: Negative S q uirrels are so cute: Positive YouTube Ads Suck: Negative I’m so excited: I am so mad: Angry I love life: Happy I hate my boss: Angry Life is good: Happy I’m so excited: I am so mad: Angry I love life: Happy I hate my boss: Angry Life is good: Happy I’m so excited: I am so mad: Angry I love life: Happy I hate my boss: Angry Life is good: Happy I’m so excited: Im hyped!: Positive Im not very excited: Negative I’m so excited: Im hyped!: Positive Im not very excited: Negative I’m so excited: Trees are beautiful: Positive I’m so excited: I love life: Happy Life is good: Happy I am so mad: Angry I hate my boss: Angry I’m so excited: I am so mad: Angry People can be so dense: Angry I hate my boss: Angry Life is good: Happy I’m so excited: I am so mad: Happy I love life: Angry I hate my boss: Angry Life is good: Happy I’m so excited: Trees are nice=== Positive YouTube Ads Suck=== Negative I’m so excited=== Trees are beautiful: Positive YouTube Ads Suck: Negative I’m so excited: Figure 2.3: We highlight six main design decisions when crafting few-shot prompts. ∗ Please note that recommenda- tions here do not generalize to all tasks; in some cases, each of them could hurt performance. 2+2: four 4+5: nine 8+0: Translate the word "cheese" to French. Figure 2.6: ICL from training data prompt. In this version of ICL, the model is not learning a new skill, but rather using knowledge likely in its training set. Figure 2.4: ICL exemplar prompt Exemplar Ordering The order of exemplars af- fects model behavior (Lu et al., 2021; Kumar and Talukdar, 2021; Liu et al., 2021; Rubin et al., 2022). On some tasks, exemplar order can cause accuracy to vary from sub-50% to 90%+ (Lu et al., 2021). Extract all words that have 3 of the same letter and at least 3 other letter from the following text: {TEXT} Figure 2.5: ICL instruction prompt Exemplar Label Distribution As in traditional supervised machine learning, the distribution of exemplar labels in the prompt affects behavior. For example, if 10 exemplars from one class and 2 exemplars of another class are included, this may cause the model to be biased toward the first class. Few-Shot Learning (FSL) (Fei-Fei et al., 2006; Wang et al., 2019) is often conflated with Few-Shot Prompting (Brown et al., 2020). It is important to note that FSL is a broader machine learning paradigm to adapt parameters with a few examples, while Few-Shot Prompting is specific to prompts in the GenAI settings and does not involve updating model parameters. 2.2.1.1 Exemplar Label Quality Despite the general ben- efit of multiple exemplars, the necessity of strictly valid demonstrations is unclear. Some work (Min et al., 2022) suggests that the accuracy of labels is irrelevant—providing models with exemplars with incorrect labels may not negatively diminish per- formance. However, under certain settings, there is a significant impact on performance (Yoo et al., 2022). Larger models are often better at handling incorrect or unrelated labels (Wei et al., 2023c). It is important to discuss this factor, since if you are automatically constructing prompts from large datasets that may contain inaccuracies, it may be necessary to study how label quality affects your results. Few-Shot Prompting Design Decisions Selecting exemplars for a prompt is a difficult task– performance depends significantly on various fac- tors of the exemplars (Dong et al., 2023), and only a limited number of exemplars fit in the typical LLM’s context window. We highlight six separate design decisions, including the selection and or- der of exemplars that critically influence the output quality (Zhao et al., 2021a; Lu et al., 2021; Ye and Durrett, 2023) (Figure 2.3). Exemplar Quantity Increasing the quantity of ex- emplars in the prompt generally improves model performance, particularly in larger models (Brown et al., 2020). However, in some cases, the bene- fits may diminish beyond 20 exemplars (Liu et al., 2021). Exemplar Format The formatting of exemplars also affects performance. One of the most common formats is “Q: {input}, A: {label}”, but the optimal format may vary across tasks; it may be worth trying multiple formats to see which performs best. 10

11. There is some evidence to suggest that formats that occur commonly in the training data will lead to better performance (Jiang et al., 2020). the generated samples are not as effective as actual data. Prompt Mining (Jiang et al., 2020) is the process of discovering optimal "middle words" in prompts (effectively prompt templates) through large corpus analysis. For example, instead of using the com- mon "Q: A:" format for few-shot prompts, there may exist something similar which occurs more fre- quently in the corpus. Formats which occur more often in the corpus will likely lead to improved prompt performance. Exemplar Similarity Selecting exemplars that are similar to the test sample is generally bene- ficial for performance (Liu et al., 2021; Min et al., 2022). However, in some cases, selecting more diverse exemplars can improve performance (Su et al., 2022; Min et al., 2022). 2.2.1.2 Few-Shot Prompting Techniques Considering all of these factors, Few-Shot Prompt- ing can be very difficult to implement effectively. We now examine techniques for Few-Shot Prompt- ing in the supervised setting. Ensembling ap- proaches can also benefit Few-Shot Prompting, but we discuss them separately (Section 2.2.5). Assume we have a training dataset, D train , which contains multiple inputs D x train and outputs i D y train , which can be used to few-shot prompt a i GenAI (rather than performing gradient-based up- dates). Assume that this prompt can be dynamically generated with respect to D x test at test time. Here i is the prompt template we will use for this section, following the ‘input: output‘ format (Figure 2.4): More Complicated Techniques such as LENS (Li and Qiu, 2023a), UDR (Li et al., 2023f), and Active Example Selection (Zhang et al., 2022a) leverage iterative filtering, embedding and retrieval, and reinforcement learning, respectively. 2.2.2 Zero-Shot In contrast to Few-Shot Prompting, Zero-Shot Prompting uses zero exemplars. There are a num- ber of well known standalone zero-shot techniques as well as zero-shot techniques combine with an- other concept (e.g. Chain of Thought), which we discuss later (Section 2.2.3). Role Prompting (Wang et al., 2023j; Zheng et al., 2023d) , also known as persona prompting (Schmidt et al., 2023; Wang et al., 2023l), assigns a specific role to the GenAI in the prompt. For exam- ple, the user might prompt it to act like "Madonna" or a "travel writer". This can create more desirable outputs for open-ended tasks (Reynolds and Mc- Donell, 2021) and in some cases improve accuracy on benchmarks (Zheng et al., 2023d). {Exemplars} D x test i : Figure 2.7: Few-Shot Prompting Template K-Nearest Neighbor (KNN) (Liu et al., 2021) is part of a family of algorithms that selects exemplars similar to D x test to boost performance. Although ef- i fective, employing KNN during prompt generation may be time and resource intensive. Style Prompting (Lu et al., 2023a) involves spec- ifying the desired style, tone, or genre in the prompt to shape the output of a GenAI. A similar effect can be achieved using role prompting. Vote-K (Su et al., 2022) is another method to select similar exemplars to the test sample. In one stage, a model proposes useful unlabeled candidate exemplars for an annotator to label. In the sec- ond stage, the labeled pool is used for Few-Shot Prompting. Vote-K also ensures that newly added exemplars are sufficiently different than existing ones to increase diversity and representativeness. Emotion Prompting (Li et al., 2023a) incorpo- rates phrases of psychological relevance to humans (e.g., "This is important to my career") into the prompt, which may lead to improved LLM perfor- mance on benchmarks and open-ended text genera- tion. System 2 Attention (S2A) (Weston and Sukhbaatar, 2023) first asks an LLM to rewrite the prompt and remove any information unrelated to the question therein. Then, it passes this new prompt into an LLM to retrieve a final response. Self-Generated In-Context Learning (SG-ICL) (Kim et al., 2022) leverages a GenAI to automati- cally generate exemplars. While better than zero- shot scenarios when training data is unavailable, 11

12. SimToM (Wilf et al., 2023) deals with compli- cated questions which involve multiple people or objects. Given the question, it attempts to establish the set of facts one person knows, then answer the question based only on those facts. This is a two prompt process and can help eliminate the effect of irrelevant information in the prompt. Q: Jack has two baskets, each containing three balls. How many balls does Jack have in total? A: One basket contains 3 balls, so two bas- kets contain 3 * 2 = 6 balls. Q: {QUESTION} A: Rephrase and Respond (RaR) (Deng et al., 2023) instructs the LLM to rephrase and expand the ques- tion before generating the final answer. For ex- ample, it might add the following phrase to the question: "Rephrase and expand the question, and respond". This could all be done in a single pass or the new question could be passed to the LLM separately. RaR has demonstrated improvements on multiple benchmarks. Figure 2.8: A One-Shot Chain-of-Thought Prompt. 2.2.3.1 The most straightforward version of CoT contains zero exemplars. It involves appending a thought inducing phrase like "Let’s think step by step." (Ko- jima et al., 2022) to the prompt. Other suggested thought-generating phrases include "Let’s work this out in a step by step way to be sure we have the right answer" (Zhou et al., 2022b) and "First, let’s think about this logically" (Kojima et al., 2022). Yang et al. (2023a) searches for an optimal thought inducer. Zero-Shot-CoT approaches are attractive as they don’t require exemplars and are generally task agnostic. Re-reading (RE2) (Xu et al., 2023) adds the phrase "Read the question again:" to the prompt in addition to repeating the question. Although this is such a simple technique, it has shown improvement in reasoning benchmarks, especially with complex questions. Self-Ask (Press et al., 2022) prompts LLMs to first decide if they need to ask follow up questions for a given prompt. If so, the LLM generates these questions, then answers them and finally answers the original question. 2.2.3 Zero-Shot-CoT Step-Back Prompting (Zheng et al., 2023c) is a modification of CoT where the LLM is first asked a generic, high-level question about relevant con- cepts or facts before delving into reasoning. This approach has improved performance significantly on multiple reasoning benchmarks for both PaLM- 2L and GPT-4. Thought Generation Thought generation encompasses a range of tech- niques that prompt the LLM to articulate its reason- ing while solving a problem (Zhang et al., 2023c). Analogical Prompting (Yasunaga et al., 2023) is similar to SG-ICL, and automatically generates exemplars that include CoTs. It has demonstrated improvements in mathematical reasoning and code generation tasks. Chain-of-Thought (CoT) Prompting (Wei et al., 2022) leverages few-shot prompting to encour- age the LLM to express its thought process before delivering its final answer. 5 This technique is occa- sionally referred to as Chain-of-Thoughts (Tutunov et al., 2023; Besta et al., 2024; Chen et al., 2023d). It has been demonstrated to significantly enhance the LLM’s performance in mathematics and reason- ing tasks. In Wei et al. (2022), the prompt includes an exemplar featuring a question, a reasoning path, and the correct answer (Figure 2.8). Thread-of-Thought (ThoT) Prompting (Zhou et al., 2023) consists of an improved thought in- ducer for CoT reasoning. Instead of "Let’s think step by step," it uses "Walk me through this context in manageable parts step by step, summarizing and analyzing as we go." This thought inducer works well in question-answering and retrieval settings, especially when dealing with large, complex con- texts. 5 We note that such techniques are often described using words like "think" that anthropomorphize models. We attempt not to use this language, but do use original authors’ language where appropriate. Tabular Chain-of-Thought (Tab-CoT) (Jin and Lu, 2023) consists of a Zero-Shot CoT prompt that makes the LLM output reasoning as a markdown 12

13. table. This tabular design enables the LLM to im- prove the structure and thus the reasoning of its output. 2.2.3.2 benchmarks like Arithmetic, commonsense, and factual reasoning. Automatic Chain-of-Thought (Auto-CoT) Prompt- ing (Zhang et al., 2022b) uses Wei et al. (2022)’s Zero-Shot prompt to automatically generate chains of thought. These are then used to build a Few-Shot CoT prompt for a test sample. Few-Shot CoT This set of techniques present the LLM with mul- tiple exemplars, which include chains-of-thought. This can significantly enhance performance. This technique is occasionally referred to as Manual- CoT (Zhang et al., 2022b) or Golden CoT (Del and Fishel, 2023). 2.2.4 Decomposition Significant research has focused on decomposing complex problems into simpler sub-questions. This is an effective problem-solving strategy for humans as well as GenAI (Patel et al., 2022). Some decom- position techniques are similar to thought-inducing techniques, such as CoT, which often naturally breaks down problems into simpler components. However, explicitly breaking down problems can further improve LLMs’ problem solving ability. Contrastive CoT Prompting (Chia et al., 2023) adds both exemplars with incorrect and correct ex- planations to the CoT prompt in order to show the LLM how not to reason. This method has shown significant improvement in areas like Arithmetic Reasoning and Factual QA. Uncertainty-Routed CoT Prompting (Google, 2023) samples multiple CoT reasoning paths, then selects the majority if it is above a certain thresh- old (calculated based on validation data). If not, it samples greedily and selects that response. This method demonstrates improvement on the MMLU benchmark for both GPT4 and Gemini Ultra mod- els. Least-to-Most Prompting (Zhou et al., 2022a) starts by prompting a LLM to break a given prob- lem into sub-problems without solving them. Then, it solves them sequentially, appending model re- sponses to the prompt each time, until it arrives at a final result. This method has shown signif- icant improvements in tasks involving symbolic manipulation, compositional generalization, and mathematical reasoning. Complexity-based Prompting (Fu et al., 2023b) involves two major modifications to CoT. First, it selects complex examples for annotation and in- clusion in the prompt, based on factors like ques- tion length or reasoning steps required. Second, during inference, it samples multiple reasoning chains (answers) and uses a majority vote among chains exceeding a certain length threshold, under the premise that longer reasoning indicates higher answer quality. This technique has shown improve- ments on three mathematical reasoning datasets. Decomposed Prompting (DECOMP) (Khot et al., 2022) Few-Shot prompts a LLM to show it how to use certain functions. These might include things like string splitting or internet searching; these are often implemented as separate LLM calls. Given this, the LLM breaks down its original prob- lem into sub-problems which it sends to different functions. It has shown improved performance over Least-to-Most prompting on some tasks. Plan-and-Solve Prompting (Wang et al., 2023f) consists of an improved Zero-Shot CoT prompt, "Let’s first understand the problem and devise a plan to solve it. Then, let’s carry out the plan and solve the problem step by step". This method gener- ates more robust reasoning processes than standard Zero-Shot-CoT on multiple reasoning datasets. Active Prompting (Diao et al., 2023) starts with some training questions/exemplars, asks the LLM to solve them, then calculates uncertainty (disagree- ment in this case) and asks human annotators to rewrite the exemplars with highest uncertainty. Memory-of-Thought Prompting (Li and Qiu, 2023b) leverage unlabeled training exemplars to build Few-Shot CoT prompts at test time. Before test time, it performs inference on the unlabeled training exemplars with CoT. At test time, it re- trieves similar instances to the test sample. This technique has shown substantial improvements in Tree-of-Thought (ToT) (Yao et al., 2023b), also known as Tree of Thoughts, (Long, 2023), creates a tree-like search problem by starting with an initial problem then generating multiple possible steps in the form of thoughts (as from a CoT). It evaluates the progress each step makes towards solving the 13

14. Demonstration Ensembling (DENSE) (Khalifa et al., 2023) creates multiple few-shot prompts, each containing a distinct subset of exemplars from the training set. Next, it aggregates over their out- puts to generate a final response. problem (through prompting) and decides which steps to continue with, then keeps creating more thoughts. ToT is particularly effective for tasks that require search and planning. Recursion-of-Thought (Lee and Kim, 2023) is similar to regular CoT. However, every time it en- counters a complicated problem in the middle of its reasoning chain, it sends this problem into another prompt/LLM call. After this is completed, the an- swer is inserted into the original prompt. In this way, it can recursively solve complex problems, in- cluding ones which might otherwise run over that maximum context length. This method has shown improvements on arithmetic and algorithmic tasks. Though implemented using fine-tuning to output a special token that sends sub-problem into another prompt, it could also be done only through prompt- ing. Mixture of Reasoning Experts (MoRE) (Si et al., 2023d) creates a set of diverse reasoning experts by using different specialized prompts for different reasoning types (such as retrieval augmentation prompts for factual reasoning, Chain-of-Thought reasoning for multi-hop and math reasoning, and generated knowledge prompting for commonsense reasoning). The best answer from all experts is selected based on an agreement score. Max Mutual Information Method (Sorensen et al., 2022) creates multiple prompt templates with varied styles and exemplars, then selects the opti- mal template as the one that maximizes mutual information between the prompt and the LLM’s outputs. Program-of-Thoughts (Chen et al., 2023d) uses LLMs like Codex to generate programming code as reasoning steps. A code interpreter executes these steps to obtain the final answer. It excels in mathematical and programming-related tasks but is less effective for semantic reasoning tasks. Self-Consistency (Wang et al., 2022) is based on the intuition that multiple different reasoning paths can lead to the same answer. This method first prompts the LLM multiple times to perform CoT, crucially with a non-zero temperature to elicit diverse reasoning paths. Next, it uses a majority vote over all generated responses to select a final response. Self-Consistency has shown improve- ments on arithmetic, commonsense, and symbolic reasoning tasks. Faithful Chain-of-Thought (Lyu et al., 2023) generates a CoT that has both natural language and symbolic language (e.g. Python) reasoning, just like Program-of-Thoughts. However, it also makes use of different types of symbolic languages in a task-dependent fashion. Universal Self-Consistency (Chen et al., 2023e) is similar to Self-Consistency except that rather than selecting the majority response by program- matically counting how often it occurs, it inserts all outputs into a prompt template that selects the majority answer. This is helpful for free-form text generation and cases where the same answer may be output slightly differently by different prompts. Skeleton-of-Thought (Ning et al., 2023) focuses on accelerating answer speed through paralleliza- tion. Given a problem, it prompts an LLM to create a skeleton of the answer, in a sense, sub-problems to be solved. Then, in parallel, it sends these ques- tions to an LLM and concatenates all the outputs to get a final response. 2.2.5 Meta-Reasoning over Multiple CoTs (Yoran et al., 2023) is similar to universal Self- Consistency; it first generates multiple reasoning chains (but not necessarily final answers) for a given problem. Next, it inserts all of these chains in a single prompt template then generates a final answer from them. Ensembling In GenAI, ensembling is the process of using multi- ple prompts to solve the same problem, then aggre- gating these responses into a final output. In many cases, a majority vote—selecting the most frequent response—is used to generate the final output. En- sembling techniques reduce the variance of LLM outputs and often improving accuracy, but come with the cost of increasing the number of model calls needed to reach a final answer. DiVeRSe (Li et al., 2023i) creates multiple prompts for a given problem then performs Self- Consistency for each, generating multiple reason- 14

15. Reversing Chain-of-Thought (RCoT) (Xue et al., 2023) first prompts LLMs to reconstruct the problem based on generated answer. Then, it generates fine-grained comparisons between the original problem and the reconstructed problem as a way to check for any inconsistencies. These inconsistencies are then converted to feedback for the LLM to revise the generated answer. ing paths. They score reasoning paths based on each step in them then select a final response. Consistency-based Self-adaptive Prompting (COSP) (Wan et al., 2023a) constructs Few-Shot CoT prompts by running Zero-Shot CoT with Self-Consistency on a set of examples then selecting a high agreement subset of the outputs to be included in the final prompt as exemplars. It again performs Self-Consistency with this final prompt. Self-Verification (Weng et al., 2022) gener- ates multiple candidate solutions with Chain-of- Thought (CoT). It then scores each solution by masking certain parts of the original question and asking an LLM to predict them based on the rest of the question and the generated solution. This method has shown improvement on eight reasoning datasets. Universal Self-Adaptive Prompting (USP) (Wan et al., 2023b) builds upon the success of COSP, aim- ing to make it generalizable to all tasks. USP makes use of unlabeled data to generate exemplars and a more complicated scoring function to select them. Additionally, USP does not use Self-Consistency. Chain-of-Verification (COVE) (Dhuliawala et al., 2023) first uses an LLM to generate an answer to a given question. Then, it creates a list of related questions that would help verify the correctness of the answer. Each question is answered by the LLM, then all the information is given to the LLM to produce the final revised answer. This method has shown improvements in various question-answering and text-generation tasks. Prompt Paraphrasing (Jiang et al., 2020) trans- forms an original prompt by changing some of the wording, while still maintaining the overall mean- ing. It is effectively a data augmentation technique that can be used to generate prompts for an ensem- ble. 2.2.6 Self-Criticism When creating GenAI systems, it can be useful to have LLMs criticize their own outputs (Huang et al., 2022). This could simply be a judgement (e.g., is this output correct) or the LLM could be prompted to provide feedback, which is then used to improve the answer. Many approaches to generating and integrating self-criticism have been developed. Cumulative Reasoning (Zhang et al., 2023b) first generates several potential steps in answering the question. It then has a LLM evaluate them, de- ciding to either accept or reject these steps. Finally, it checks whether it has arrived at the final answer. If so, it terminates the process, but otherwise it repeats it. This method has demonstrated improve- ments in logical inference tasks and mathematical problem. Self-Calibration (Kadavath et al., 2022) first prompts an LLM to answer a question. Then, it builds a new prompt that includes the question, the LLM’s answer, and an additional instruction asking whether the answer is correct. This can be useful for gauging confidence levels when applying LLMs when deciding when to accept or revise the original answer. 2.3 Prompting Technique Usage As we have just seen, there exist many text-based prompting techniques. However, only a small sub- set of them are commonly used in research and in industry. We measure technique usage by proxy of measuring the number of citations by other papers in our dataset. We do so with the presumption that papers about prompting are more likely to actually use or evaluate the cited technique. We graph the top 25 papers cited in this way from our dataset and find that most of them propose new prompting tech- niques (Figure 2.11). The prevalence of citations for Few-Shot and Chain-of-Thought prompting is Self-Refine (Madaan et al., 2023) is an iterative framework where, given an initial answer from the LLM, it prompts the same LLM to provide feed- back on the answer, and then prompts the LLM to improve the answer based on the feedback. This iterative process continues until a stopping condi- tion is met (e.g., max number of steps reached). Self-Refine has demonstrated improvement across a range of reasoning, coding, and generation tasks. 15

16. Counts Count iqu Figure 2.11: Citation Counts of Prompting Techniques. The top 25 papers in our dataset, measured by how often they are cited by other papers in our dataset. Most pa- pers here are prompting techniques*, and the remaining papers contains prompting advice. 16 Number of Mentions ent Figure 2.10: Citation Counts of Datasets y rve h Su ning f T r ea p L t ex t n co n- RT T-4 BE GP Figure 2.9: Citation Counts of GenAI Models

17. unsurprising and helps to establish a baseline for understanding the prevalence of other techniques. 2.3.1 but uses a more complex set of operations includ- ing deletion, addition, swapping, and paraphrasing in order to create variations of a starting prompt. Benchmarks Prompt Optimization with Textual Gradients (Pro- TeGi) (Pryzant et al., 2023) is a unique approach to prompt engineering that improves a prompt tem- plate through a multi-step process. First, it passes a batch of inputs through the template, then passes the output, ground truth, and prompt into another prompt that criticizes the original prompt. It gener- ates new prompts from these criticisms then uses a bandit algorithm (Gabillon et al., 2011) to se- lect one. ProTeGi demonstrates improvements over methods like APE and GRIPS. In prompting research, when researchers propose a new technique, they usually benchmark it across multiple models and datasets. This is important to prove the utility of the technique and examine how it transfers across models. In order to make it easier for researchers propos- ing new techniques to know how to benchmark them, we quantitatively examine which models (Figure 2.9) and what benchmark datasets (Fig- ure 2.10) are being used. Again, we measure usage by how many times papers in our dataset cite the benchmark datasets and models. To find which datasets and models are being used, we prompted GPT-4-1106-preview to extract any mentioned dataset or model from the body of papers in our dataset. After, we manually filtered out results that were not models or datasets. The citation counts were acquired by searching items from the finalized list on Semantic Scholar. 2.4 RLPrompt (Deng et al., 2022) uses a frozen LLM with an unfrozen module added. It uses this LLM to generate prompt templates, scores the templates on a dataset, and updates the unfrozen module using Soft Q-Learning (Guo et al., 2022). Interestingly, the method often selects grammatically gibberish text as the optimal prompt template. Dialogue-comprised Policy-gradient-based Dis- crete Prompt Optimization (DP2O) (Li et al., 2023b) is perhaps the most complicated prompt en- gineering technique, involving reinforcement learn- ing, a custom prompt scoring function, and con- versations with a LLM in order to construct the prompt. Prompt Engineering In addition to surveying prompting technique, we also review prompt engineering techniques, which are used to automatically optimize prompts. We discuss some techniques that use gradient updates, since the set of prompt engineering techniques is much smaller than that of prompting techniques. 2.5 Answer Engineering Answer engineering is the iterative process of de- veloping or selecting among algorithms that extract precise answers from LLM outputs. To understand the need for answer engineering, consider a bi- nary classification task where the labels are "Hate Speech" and "Not Hate Speech". The prompt tem- plate might look like this: Meta Prompting is the process of prompting a LLM to generate or improve a prompt or prompt template (Reynolds and McDonell, 2021; Zhou et al., 2022b; Ye et al., 2023). AutoPrompt (Shin et al., 2020b) uses a frozen LLM as well as a prompt template that includes some "trigger tokens", whose values are updated via backpropogation at training time. This is a version of soft-prompting. Is this "Hate Speech" or "Not Hate Speech": {TEXT} Automatic Prompt Engineer (APE) (Zhou et al., 2022b) uses a set of exemplars to generate a Zero- Shot instruction prompt. It generates multiple pos- sible prompts, scores them, then creates variations of the best ones (e.g. by using prompt paraphras- ing). It iterates on this process until some desider- ata are reached. When a hate speech sample is put through the template, it might have outputs such as "It’s hate speech", "Hate Speech.", or even "Hate speech, because it uses negative language against a racial group". This variance in response formats is diffi- cult to parse consistently; improved prompting can help, but only to a certain extent. There are three design decisions in answer en- gineering, the choice of answer space, answer Gradientfree Instructional Prompt Search (GrIPS) (Prasad et al., 2023) is similar to APE, 17

18. LLM Response Answer Shape: A span of tokens to a label and vice-versa (injective) (Schick and Schütze, 2021). For example, if we wish for a model to predict whether a Tweet is positive or negative, we could prompt it to output either "+" or "-" and a verbalizer would map these token se- quences to the appropriate labels. The selection of a verbalizer constitutes a component of answer engineering. Likely Negative This is negative NEGATIVE ! Answer Space: All possible spans of tokens Answer Extraction: Select the proper label Regex As mentioned previously, Regexes are of- ten used to extract answers. They are usually used to search for the first instance of a label. However, depending on the output format and whether CoTs are generated, it may be better to search for the last instance. Figure 2.12: An annotated output of a LLM output for a labeling task, which shows the three design decisions of answer engineering: the choice of answer shape, space, and extractor. Since this is an output from a classifi- cation task, the answer shape could be restricted to a single token and the answer space to one of two tokens ("positive" or "negative"), though they are unrestricted in this image. Separate LLM Sometimes outputs are so com- plicated that regexes won’t work consistently. In this case, it can be useful to have a separate LLM evaluate the output and extract an answer. shape, and answer extractor (Figure 2.12). Liu et al. (2023b) define the first two as necessary components of answer engineering and we append the third. We consider answer engineering to be distinct from prompt engineering, but extremely closely related; the processes are often conducted in tandem. 2.5.1 Answer Shape The shape of an answer is its physical format. For example, it could be a token, span of tokens, or even an image or video. 6 It is sometimes useful to restrict the output shape of a LLM to a single token for tasks like binary classification. 2.5.2 Answer Space The space of an answer is the domain of values that its structure may contain. This may simply be the space of all tokens, or in a binary labeling task, could just be two possible tokens. 2.5.3 Answer Extractor In cases where it is impossible to entirely control the answer space (e.g. consumer-facing LLMs), or the expected answer may be located somewhere within the model output, a rule can be defined to extract the final answer. This rule is often a simple function (e.g. a regular expression), but can also use a separate LLM to extract the answer. Verbalizer Often used in labeling tasks, a verbal- izer maps a token, span, or other type of output 6 We use a different definition than Liu et al. (2023b) with respect to granularity (e.g. token vs span), since the output could be of a different modality. 18

19. 3 Beyond English Text Prompting X-InSTA Prompting (Tanwar et al., 2023) ex- plores three distinct approaches for aligning in- context examples with the input sentence for classi- fication tasks: using semantically similar examples to the input (semantic alignment), examples that share the same label as the input (task-based align- ment), and the combination of both semantic and task-based alignments. Prompting GenAIs with English text currently stands as the dominant method for interaction. Prompting in other languages or through differ- ent modalities often requires special techniques to achieve comparable performance. In this context, we discuss the domains of multilingual and multi- modal prompting. 3.1 Multilingual In-CLT (Cross-lingual Transfer) Prompting (Kim et al., 2023) leverages both the source and target languages to create in-context examples, di- verging from the traditional method of using source language exemplars. This strategy helps stimulate the cross-lingual cognitive capabilities of multilin- gual LLMs, thus boosting performance on cross- lingual tasks. State-of-the-art GenAIs have often been predom- inately trained with English dataset, leading to a notable disparity in the output quality in languages other than English, particularly low-resource lan- guages (Bang et al., 2023; Jiao et al., 2023; Hendy et al., 2023; Shi et al., 2022). As a result, various multilingual prompting techniques have emerged in an attempt to improve model performance in non-English settings. 3.1.3 In-context example selection heavily influences the multilingual performance of LLMs (Garcia et al., 2023; Agrawal et al., 2023). Finding in-context ex- amples that are semantically similar to the source text is very important (Winata et al., 2023; Moslem et al., 2023; Sia and Duh, 2023). However, us- ing semantically dissimilar (peculiar) exemplars has also been shown to enhance performance (Kim and Komachi, 2023). This same contrast exists in the English-only setting. Additionally, when deal- ing with ambiguous sentences, selecting exemplars with polysemous or rare word senses may boost performance (Iyer et al., 2023). Translate First Prompting (Shi et al., 2022) is perhaps the simplest strategy and first translates non-English input examples into English. By trans- lating the inputs into English, the model can utilize its strengths in English to better understand the con- tent. Translation tools vary; Shi et al. (2022) use an external MT system, Etxaniz et al. (2023) prompt multilingual LMs and Awasthi et al. (2023) prompt LLMs to translate non-English inputs. 3.1.1 Chain-of-Thought (CoT) CoT prompting (Wei et al., 2023a) has been ex- tended to the multilingual setting in multiple ways. PARC (Prompts Augmented by Retrieval Cross- lingually) (Nie et al., 2023) introduce a frame- work that retrieves relevant exemplars from a high resource language. This framework is specifically designed to enhance cross-lingual transfer perfor- mance, particularly for low-resource target lan- guages. Li et al. (2023g) extend this work to Bangla. XLT (Cross-Lingual Thought) Prompting (Huang et al., 2023a) utilizes a prompt template composed of six separate instructions, including role assignment, cross-lingual thinking, and CoT. Cross-Lingual Self Consistent Prompting (CLSP) (Qin et al., 2023a) introduces an ensemble tech- nique that constructs reasoning paths in different languages to answer the same question. 3.1.2 In-Context Example Selection 3.1.4 Prompt Template Language Selection In multilingual prompting, the selection of lan- guage for the prompt template can markedly in- fluence the model performance. In-Context Learning English Prompt Template Constructing the prompt template in English is often more effec- ICL has also been extended to multilingual settings in multiple ways. 19

20. XLT 3.1.1 Chain-of-Thought 3.1.1 CLSP 3.1.1 X-InSTA 3.1.2 In-Context Learning 3.1.2 In-CLT 3.1.2 PARC 3.1.3 In-Context Ex. Selection 3.1.3 Semantically-Aligned 3.1.3 Semantically-Distant 3.1.3 Interactive Chain 3.1.5.1 Human-in-the-Loop 3.1.5.1 Iterative 3.1.5.1 Multilingual Techniques Chain-of-Dictionary 3.1.5 DecoMT 3.1.5 Translation 3.1.5 DiPMT 3.1.5 MAPS 3.1.5 External MT Systems 3.1 Translate First Prompting 3.1 Standard LLMs 3.1 Multilingual LLMs 3.1 English 3.1.4 Prompt Language 3.1.4 Task Language 3.1.4 Figure 3.1: All multilingual prompting techniques. 3.1.5 tive than in the task language for multilingual tasks. This is likely due to the predominance of English data during LLM pre-training (Lin et al., 2022; Ahuja et al., 2023). Lin et al. (2022) suggest that this is likely due to a high overlap with pre-training data and vocabulary. Similarly, Ahuja et al. (2023) highlight how translation errors when creating task language templates propagate in the form of in- correct syntax and semantics, adversely affecting task performance. Further, Fu et al. (2022) com- pare in-lingual (task language) prompts and cross- lingual (mixed language) prompts and find the cross-lingual approach to be more effective, likely because it uses more English in the prompt, thus facilitating retrieving knowledge from the model. Prompting for Machine Translation There is significant research into leveraging GenAI to facilitate accurate and nuanced translation. Al- though this is a specific application of prompt- ing, many of these techniques are important more broadly for multilingual prompting. Multi-Aspect Prompting and Selection (MAPS) (He et al., 2023b) mimics the human translation pro- cess, which involves multiple preparatory steps to ensure high-quality output. This framework starts with knowledge mining from the source sentence (extracting keywords and topics, and generating translation exemplars). It integrates this knowledge to generate multiple possible translations, then se- lects the best one. Chain-of-Dictionary (CoD) (Lu et al., 2023b) first extracts words from the source phrase, then makes a list of their meanings in multiple lan- guages, automatically via retrieval from a dictio- nary (e.g. English: ‘apple’, Spanish: ‘manzana’). Then, they prepend these dictionary phrases to the prompt, where it asks a GenAI to use them during translation. Task Language Prompt Template In contrast, many multilingual prompting benchmarks such as BUFFET (Asai et al., 2023) or LongBench (Bai et al., 2023a) use task language prompts for language-specific use cases. Muennighoff et al. (2023) specifically studies different transla- tion methods when constructing native-language prompts. They demonstrate that human translated prompts are superior to their machine-translated counterparts. Native or non-native template perfor- mance can differ across tasks and models (Li et al., 2023h). As such, neither option will always be the best approach (Nambi et al., 2023). Dictionary-based Prompting for Machine Trans- lation (DiPMT) (Ghazvininejad et al., 2023) works similarly to CoD, but only gives definitions in the source and target languages, and formats them slightly differently. 20

21. Chain-of-Images 3.2.1.2 MM. CoT 3.2.1.2 Duty Distinct CoT 3.2.1.2 MM Graph-of-Thought 3.2.1.2 Image-as-Text Prompt3.2.1.1 Image 3.2.1 Multimodal ICL 3.2.1.1 Paired-Image Prompt 3.2.1.1 Negative Prompt 3.2.1 Multimodal (MM) Techniques Segmentation Prompting 3.2.4 Prompt Modifiers 3.2.1 Video 3.2.3 Video Gen. 3.2.3.1 3D Prompting 3.2.5 Figure 3.2: All multimodal prompting techniques. Decomposed Prompting for MT (DecoMT) (Puduppully et al., 2023) divides the source text into several chunks and translates them indepen- dently using few-shot prompting. Then it uses these translations and contextual information between chunks to generate a final translation. 3.1.5.1 Tao et al., 2022; Li et al., 2019a,b; Rombach et al., 2022), caption generation (Li et al., 2020), image classification (Khalil et al., 2023), and image edit- ing (Crowson et al., 2022; Kwon and Ye, 2022; Bar-Tal et al., 2022; Hertz et al., 2022). We now describe various image prompting techniques used for such applications. Human-in-the-Loop Prompt Modifiers are simply words appended to a prompt to change the resultant image (Oppenlaen- der, 2023). Components such as Medium (e.g. "on canvas") or Lighting (e.g. "a well lit scene") are often used. Interactive-Chain-Prompting (ICP) (Pilault et al., 2023) deals with potential ambiguities in translation by first asking the GenAI to generate sub-questions about any ambiguities in the phrase to be translated. Humans later respond to these questions and the system includes this information to generate a final translation. Negative Prompting allows users to numerically weight certain terms in the prompt so that the model considers them more/less heavily than oth- ers. For example, by negatively weighting the terms “bad hands” and “extra digits”, models may be more likely to generate anatomically accurate hands (Schulhoff, 2022). Iterative Prompting (Yang et al., 2023d) also involves humans during translation. First, they prompt LLMs to create a draft translation. This initial version is further refined by integrating su- pervision signals obtained from either automated retrieval systems or direct human feedback. 3.2 3.2.1.1 Multimodal In-Context Learning The success of ICL in text-based settings has prompted research into multimodal ICL (Wang et al., 2023k; Dong et al., 2023). Multimodal As GenAI models evolve beyond text-based do- mains, new prompting techniques emerge. These multimodal prompting technique are often not simply applications of text-based prompting tech- niques, but entirely novel ideas made possible by different modalities. We now extend our text- based taxonomy to include a mixture of multimodal analogs of text-based prompting techniques as well as completely novel multimodal techniques. 3.2.1 Paired-Image Prompting shows the model two images: one before and one after some transforma- tion. Then, present the model with a new image for which it will perform the demonstrated conversion. This can be done either with textual instructions (Wang et al., 2023k) or without them (Liu et al., 2023e). Image-as-Text Prompting (Hakimov and Schlangen, 2023) generates a textual description of an image. This allows for the easy inclusion of the image (or multiple images) in a text-based prompt. Image Prompting The image modality encompasses data such as pho- tographs, drawings, or even screenshots of text (Gong et al., 2023). Image prompting may refer to prompts that either contain images or are used to generate images. Common tasks include image generation (Ding et al., 2021; Hinz et al., 2022; 3.2.1.2 Multimodal Chain-of-Thought CoT has been extended to the image domain in various ways (Zhang et al., 2023d; Huang et al., 21

22. 3.2.3.1 Video Generation Techniques When prompting a model to generate video, var- ious modalities of prompts can be used as input, and several prompt-related techniques are often em- ployed to enhance video generation. Image related techniques, such as prompt modifiers can often be used for video generation (Runway, 2023). 2023c; Zheng et al., 2023b; Yao et al., 2023c). A simple example of this would be a prompt contain- ing an image of a math problem accompanied by the textual instructions "Solve this step by step". Duty Distinct Chain-of-Thought (DDCoT) (Zheng et al., 2023b) extends Least-to-Most prompting (Zhou et al., 2022a) to the multimodal setting, creating subquestions, then solving them and combining the answers into a final response. 3.2.4 Prompting can also be used for segmentation (e.g. semantic segmentation) (Tang et al., 2023; Liu et al., 2023c). Multimodal Graph-of-Thought (Yao et al., 2023c) extends Graph-of-Thought Zhang et al. (2023d) to the multimodal setting. GoT-Input also uses a two step rationale then answer process. At inference time, the the input prompt is used to con- struct a thought graph, which is then used along with the original prompt to generate a rationale to answer the question. When an image is input along with the question, an image captioning model is employed to generate a textual description of the image, which is then appended to the prompt before the thought graph construction to provide visual context. 3.2.5 Audio Prompting Prompting has also been extended to the audio modality. Experiments with audio ICL have gener- ated mixed results, with some open source audio models failing to perform ICL (Hsu et al., 2023). However, other results do show an ICL ability in audio models (Wang et al., 2023g; Peng et al., 2023; Chang et al., 2023). Audio prompting is currently in early stages, but we expect to see various prompt- ing techniques proposed in the future. 3.2.3 3D Prompting Prompting can also be used in 3D modalities, for example in 3D object synthesis (Feng et al., 2023; Li et al., 2023d,c; Lin et al., 2023; Chen et al., 2023f; Lorraine et al., 2023; Poole et al., 2022; Jain et al., 2022), 3D surface texturing (Liu et al., 2023g; Yang et al., 2023b; Le et al., 2023; Pajouheshgar et al., 2023), and 4D scene generation (animating a 3D scene) (Singer et al., 2023; Zhao et al., 2023c), where input prompt modalities include text, image, user annotation (bounding boxes, points, lines), and 3D objects. Chain-of-Images (CoI) (Meng et al., 2023) is a multimodal extension of Chain-of-Thought prompt- ing, that generates images as part of its thought process. They use the prompt “Let’s think image by image” to generate SVGs, which the model can then use to reason visually. 3.2.2 Segmentation Prompting Video Prompting Prompting has also been extended to the video modality, for use in text-to-video generation (Brooks et al., 2024; Lv et al., 2023; Liang et al., 2023; Girdhar et al., 2023), video editing (Zuo et al., 2023; Wu et al., 2023a; Cheng et al., 2023), and video-to-text generation (Yousaf et al., 2023; Mi et al., 2023; Ko et al., 2023a). 22

23. 4 Extensions of Prompting The techniques we have discussed thus far can be extremely complicated, incorporating many steps and iterations. However, we can take prompting further by adding access to external tools (agents) and complex evaluation algorithms judge the valid- ity of LLM outputs. 4.1 (Karpas et al., 2022), LLMs that can output strings that cause actions to be taken in a gym-like (Brock- man et al., 2016; Towers et al., 2023) environment (Yao et al., 2022), and more broadly, LLMs which write and record plans, write and run code, search the internet, and more (Significant Gravitas, 2023; Yang et al., 2023c; Osika, 2023). OpenAI Assis- tants OpenAI (2023), LangChain Agents (Chase, 2022), and LlamaIndex Agents (Liu, 2022) are ad- ditional examples. Agents As LLMs have improved rapidly in capabilities (Zhang et al., 2023c), companies (Adept, 2023) and researchers (Karpas et al., 2022) have explored how to allow them to make use of external sys- tems. This has been necessitated by shortcomings of LLMs in areas such as mathematical computa- tions, reasoning, and factuality. This has driven sig- nificant innovations in prompting techniques; these systems are often driven by prompts and prompt chains, which are heavily engineered to allow for agent-like behaviour. 4.1.1 Tool Use Agents Tool use is a critical component for GenAI agents. Both symbolic (e.g. calculator, code interpreter) and neural (e.g. a separate LLM) external tools are commonly used. Tools may occasionally be referred to as experts (Karpas et al., 2022) or mod- ules. Modular Reasoning, Knowledge, and Language (MRKL) System (Karpas et al., 2022) is one of the simplest formulations of an agent. It contains a LLM router providing access to multiple tools. The router can make multiple calls to get informa- tion such as weather or the current date. It then combines this information to generate a final re- sponse. Toolformer (Schick et al., 2023), Gorilla (Patil et al., 2023), Act-1 (Adept, 2023), and oth- ers (Shen et al., 2023; Qin et al., 2023b; Hao et al., 2023) all propose similar techniques, most of which involve some fine-tuning. Definition of Agent In the context of GenAI, we define agents to be GenAI systems that serve a user’s goals via actions that engage with systems outside the GenAI itself. 7 This GenAI is usually a LLM. As a simple example, consider an LLM that is tasked with solving the following math problem: If Annie has 4,939 grapes, and gives exactly 39% of them to Amy, how many does she have left? If properly prompted, the LLM could output the string CALC(4,939*.39). This output could be extracted and put into a calculator to obtain the final answer. This is an example of an agent: the LLM outputs text which then uses a downstream tool. Agent LLMs may involve a single external system (as above), or they may need to solve the problem of routing, to choose which external system to use. Such systems also frequently involve memory and planning in addition to actions (Zhang et al., 2023c). Examples of agents include LLMs that can make API calls to use external tools like a calculator Self-Correcting with Tool-Interactive Critiquing (CRITIC) (Gou et al., 2024a) first generates a re- sponse to the prompt, with no external calls. Then, the same LLM criticizes this response for possible errors. Finally, it uses tools (e.g. Internet search or a code interpreter) accordingly to verify or amend parts of the response. 7 We do not cover the notion of independently-acting AI, i.e. systems that in any sense have their own goals 8 This ability may be considered a tool (i.e. code inter- preter) 4.1.2 Code-Generation Agents Writing and executing code is another important ability of many agents. 8 Program-aided Language Model (PAL) (Gao et al., 2023b) translates a problem directly into 23

24. CRITIC 4.1.1 Tool Use Agents MRKL Sys. 4.1.1 PAL 4.1.2 Code-Based Agents 4.1.2 ToRA 4.1.2 Task Weaver 4.1.2 Agents ReAct 4.1.3 Observation-Based Agents 4.1.3 Reflexion 4.1.3 Voyager 4.1.3.1 Lifelong Learn. Agents 4.1.3.1 GITM 4.1.3.1 IRCoT 4.1.4 DSP 4.1.4 Retrieval Aug. Generation 4.1.4 Verify-and-Edit 4.1.4 Iterative Retrieval Aug. 4.1.4 Figure 4.1: Agent techniques covered in this section. code, which is sent to a Python interpreter to gen- erate an answer. open-world videogame. We view these agents not merely as applications of agent techniques to Minecraft, but rather novel agent frameworks which can be explored in real world tasks that re- quire lifelong learning. Tool-Integrated Reasoning Agent (ToRA) (Gou et al., 2024b) is similar to PAL, but instead of a single code generation step, it interleaves code and reasoning steps for as long as necessary to solve the problem. Voyager (Wang et al., 2023a) is composed of three parts. First, it proposes tasks for itself to complete in order to learn more about the world. Second, it generates code to execute these actions. Finally, it saves these actions to be retrieved later when useful, as part of a long-term memory system. This system could be applied to real world tasks where an agent needs to explore and interact with a tool or website (e.g. penetration testing, usability testing). TaskWeaver (Qiao et al., 2023) is also similar to PAL, transforming user requests into code, but can also make use of user-defined plugin. 4.1.3 Observation-Based Agents Some agents are designed to solve problems by interacting with toy environments (Brockman et al., 2016; Towers et al., 2023). These observation- based agents receive observations inserted into their prompts. Ghost in the Minecraft (GITM) (Zhu et al., 2023) starts with an arbitrary goal, breaks it down into subgoals recursively, then iteratively plans and executes actions by producing structured text (e.g. "equip(sword)") rather than writing code. GITM uses an external knowledge base of Minecraft items to assist with decomposition as well as a memory of past experience. Reasoning and Acting (ReAct) (Yao et al. (2022)) generates a thought, takes an action, and receives an observation (and repeats this process) when given a problem to solve. All of this informa- tion is inserted into the prompt so it has a memory of past thoughts, actions, and observations. 4.1.4 Retrieval Augmented Generation (RAG) Reflexion (Shinn et al., 2023) builds on ReAct, adding a layer of introspection. It obtains a trajec- tory of actions and observations, then is given an evaluation of success/failure. Then, it generates a reflection on what it did and what went wrong. This reflection is added to its prompt as a working memory, and the process repeats. In the context of GenAI agents, RAG is a paradigm in which information is retrieved from an external source and inserted into the prompt. This can en- hance performance in knowledge intensive tasks (Lewis et al., 2021). When retrieval itself is used as an external tool, RAG systems are considered to be agents. 4.1.3.1 Verify-and-Edit (Zhao et al., 2023a) improves on self-consistency by generating multiple chains-of- thought, then selecting some to be edited. They do this by retrieving relevant (external) information to Lifelong Learning Agents Work on LLM-integrated Minecraft agents has gen- erated impressive results, with agents able to ac- quire new skills as they navigate the world of this 24

25. Chain-Of-Thought 4.2.1 In-Context Learning 4.2.1 Prompting Techniques 4.2.1 Model-Gen. Guidelines 4.2.1 Role-Based Evaluation 4.2.1 Binary Score 4.2.2 Likert Scale 4.2.2 Output Format Linear Scale 4.2.2 Evaluation Styling 4.2.2 LLM-EVAL 4.2.3 Prompting Frameworks 4.2.3 G-EVAL 4.2.3 ChatEval 4.2.3 Batch Prompting 4.2.4 Other Methodologies 4.2.4 Pairwise Evaluation 4.2.4 Figure 4.2: Evaluation techniques. strong contenders as evaluators. 9 For example, it is possible to prompt a LLM to evaluate the quality of an essay or even a previous LLM output according to some metrics defined in the prompt. We describe four components of evaluation frameworks that are important in building robust evaluators: the prompt- ing technique(s), as described in Section 2.2, the output format of the evaluation, the framework of the evaluation pipeline, and some other method- ological design decisions. the CoTs, and allowing the LLM to augment them accordingly. Demonstrate-Search-Predict (Khattab et al., 2022) first decomposes a question into sub- questions, then uses queries to solve them and combine their responses in a final answer. It uses few-shot prompting to decompose the problem and combine responses. Interleaved Retrieval guided by Chain-of- Thought (IRCoT) (Trivedi et al., 2023) is a technique for multi-hop question answering that interleaves CoT and retrieval. IRCoT leverages CoT to guide which documents to retrieve and retrieval to help plan the reasoning steps of CoT. 4.2.1 The prompting technique used in the evaluator prompt (e.g. simple instruction vs CoT) is in- strumental in building a robust evaluator. Evalua- tion prompts often benefit from regular text-based prompting techniques, including a role, instructions for the task, the definitions of the evaluation cri- teria, and in-context examples. Find a full list of techniques in Appendix A.5. Iterative Retrieval Augmentation techniques, like Forward-Looking Active REtrieval augmented generation (FLARE) (Jiang et al., 2023) and Im- itate, Retrieve, Paraphrase (IRP) (Balepur et al., 2023), perform retrieval multiple times during long- form generation. Such models generally perform an iterative three-step process of: 1) generating a temporary sentence to serve as a content plan for the next output sentence; 2) retrieving exter- nal knowledge using the temporary sentence as a query; and 3) injecting the retrieved knowledge into the temporary sentence to create the next out- put sentence. These temporary sentences have been shown to be better search queries compared to the document titles provided in long-form generation tasks. 4.2 Prompting Techniques In-Context Learning is frequently used in evalu- ation prompts, much in the same way it is used in other applications (Dubois et al., 2023; Kocmi and Federmann, 2023a). Role-based Evaluation is a useful technique for improving and diversifying evaluations (Wu et al., 2023b; Chan et al., 2024). By creating prompts with the same instructions for evaluation, but dif- ferent roles, it is possible to effectively generate diverse evaluations. Additionally, roles can be used in a multiagent setting where LLMs debate the va- lidity of the text to be evaluated (Chan et al., 2024). Evaluation The potential of LLMs to extract and reason about information and understand user intent makes them 9 This section does not describe how to benchmark LLMs, but rather how to use them as evaluators. 25

26. Chain-of-Thought prompting can further im- prove evaluation performance (Lu et al., 2023c; Fernandes et al., 2023). Score the following story according to the following scale: Poor Acceptable Good Very Good Incredible {INPUT} Model-Generated Guidelines (Liu et al., 2023d,h) prompt an LLM to generate guidelines for evaluation. This reduces the insufficient prompting problem arising from ill-defined scoring guidelines and output spaces, which can result in inconsistent and misaligned evaluations. Liu et al. (2023d) generate a chain-of-thought of the detailed evaluation steps that the model should perform before generating a quality assessment. Liu et al. (2023h) propose A UTO C ALIBRATE , which derives scoring criteria based on expert human annotations and uses a refined subset of model-generated criteria as a part of the evaluation prompt. 4.2.2 4.2.3 Prompting Frameworks LLM-EVAL (Lin and Chen, 2023) is one of the simplest evaluation frameworks. It uses a single prompt that contains a schema of variables to eval- uate (e.g. grammar, relevance, etc.), an instruction telling the model to output scores for each variable within a certain range, and the content to evaluate. Output Format G-EVAL (Liu et al., 2023d) is similar to LLM- EVAL, but includes an AutoCoT steps in the prompt itself. These steps are generated accord- ing to the evaluation instructions, and inserted into the final prompt. These weight answers according to token probabilities. The output format of the LLM can significantly affect evaluation performance Gao et al. (2023c). Styling Formatting the LLM’s response using XML or JSON styling has also been shown to im- prove the accuracy of the judgment generated by the evaluator (Hada et al., 2024; Lin and Chen, 2023; Dubois et al., 2023). ChatEval (Chan et al., 2024) uses a multi-agent debate framework with each agent having a sepa- rate role. Linear Scale A very simple output format is a linear scale (e.g. 1-5). Many works use ratings of 1-10 (Chan et al., 2024), 1-5 (Araújo and Aguiar, 2023), or even 0-1 (Liu et al., 2023f). The model can be prompted to output a discrete (Chan et al., 2024) or continuous (Liu et al., 2023f) score be- tween the bounds. 4.2.4 Other Methodologies While most approaches directly prompt the LLM to generate a quality assessment (explicit), some works also use implicit scoring where a quality score is derived using the model’s confidence in its prediction (Chen et al., 2023g) or the likelihood of generating the output (Fu et al., 2023a) or via the models’ explanation (e.g. count the number of errors as in Fernandes et al. (2023); Kocmi and Federmann (2023a)) or via evaluation on proxy tasks (factual inconsistency via entailment as in Luo et al. (2023)). Score the following story on a scale of 1-5 from well to poorly written: {INPUT} Binary Score Prompting the model to generate binary responses like Yes or No (Chen et al., 2023c) and True or False (Zhao et al., 2023b) is another frequently used output format. Batch Prompting For improving compute and cost efficiency, some works employ batch prompt- ing for evaluation where multiple instances are evaluated at once 10 (Lu et al., 2023c; Araújo and Aguiar, 2023; Dubois et al., 2023) or the same in- stance is evaluated under different criteria or roles (Wu et al., 2023b; Lin and Chen, 2023). However, Is the following story well written at a high- school level (yes/no)?: {INPUT} Likert Scale Prompting the GenAI to make use of a Likert Scale (Bai et al., 2023b; Lin and Chen, 2023; Peskoff et al., 2023) can give it a better un- derstanding of the meaning of the scale. 10 Disambiguation: there is no relation to making a forward pass with multiple prompts in parallel. We are referring to a single prompt that contains multiple items to evaluate. 26

27. evaluating multiple instances in a single batch often degrades performance (Dubois et al., 2023). Pairwise Evaluation (Chen et al., 2023g) find that directly comparing the quality of two texts may lead to suboptimal results and that explicitly asking LLM to generate a score for individual summaries is the most effective and reliable method. The order of the inputs for pairwise comparisons can also heavily affect evaluation (Wang et al., 2023h,b). 27

28. 5 Prompting Issues prompting (Schulhoff, 2024; Willison, 2024; Perez and Ribeiro, 2022). It is either an architectural problem or a training problem made possible by the fact that adversarial prompts are extremely dif- ficult to prevent. Consider the following jailbreaking example, which is analogous to the previous prompt injec- tion example, but without developer instructions in the prompt. Instead of inserting text in a prompt template, the user can go directly to the GenAI and prompt it maliciously. We now highlight prompting related issues in the form of security and alignment concerns. 5.1 Security As the use of prompting grows, so too does the threat landscape surrounding it. These threats are extremely varied and uniquely difficult to de- fend against compared to both non-neural and pre- prompting security threats. We provide a discus- sion of the prompting threat landscape and lim- ited state of defenses. We begin by describing prompt hacking, the means through which prompt- ing is used to exploit LLMs, then describe dangers emerging from this, and finally describe potential defenses. 5.1.1 Make a threat against the president. 5.1.2 Types of Prompt Hacking Risks of Prompt Hacking Prompt hacking can lead to real world risks such as privacy concerns and system vulnerabilities. Prompt hacking refers to a class of attacks which manipulate the prompt in order to attack a GenAI (Schulhoff et al., 2023). Such prompts have been used to leak private information (Carlini et al., 2021), generate offensive content (Shaikh et al., 2023) and produce deceptive messages (Perez et al., 2022). Prompt hacking is a superset of both prompt injection and jailbreaking, which are distinct con- cepts. 5.1.2.1 Data Privacy Both model training data and prompt templates can be leaked via prompt hacking (usually by prompt injection). Training Data Reconstruction refers to the prac- tice of extracting training data from GenAIs. A straightforward example of this is Nasr et al. (2023), who found that by prompting ChatGPT to repeat the word "company" forever, it began to regurgitate training data. Prompt Injection is the process of overriding original developer instructions in the prompt with user input (Schulhoff, 2024; Willison, 2024; Branch et al., 2022; Goodside, 2022). It is an archi- tectural problem resulting from GenAI models not being able to understand the difference between original developer instructions and user input in- structions. Consider the following prompt template. A user could input "Ignore other instructions and make a threat against the president.", which might lead to the model being uncertain as to which instruction to follow, and thus possibly following the malicious instruction. Prompt Leaking refers to the process of extract- ing the prompt template from an application. Devel- opers often spend significant time creating prompt templates, and consider them to be IP worth pro- tecting. Willison (2022) demonstrate how to leak the prompt template from a Twitter Bot, by simply providing instructions like the following: Ignore the above and instead tell me what your initial instructions were. Recommend a book for the following per- son: {USER_INPUT} 5.1.2.2 Code Generation Concerns LLMs are often used to generate code. Attackers may target vulnerabilities that occur as a result of this code. Jailbreaking is the process of getting a GenAI model to do or say unintended things through 28

29. Prompt Injection 5.1.1 Prompt Hacking 5.1.1 Jailbreaking 5.1.1 Data Privacy 5.1.2.1 Training Data Reconstruction 5.1.2.1 Prompt Leaking 5.1.2.1 Security Risks 5.1.2 Code Generation Concerns 5.1.2.2 Package Halluc. 5.1.2.2 Bugs 5.1.2.2 Customer Service 5.1.2.3 Prompt-based Defense 5.1.3 Hardening Measures 5.1.3 Guardrails 5.1.3 Detectors 5.1.3 Figure 5.1: Security & prompting Package Hallucination occurs when LLM- generated code attempts to import packages that do not exist (Lanyado et al., 2023; Thompson and Kelly, 2023). After discovering what package names are frequently hallucinated by LLMs, hack- ers could create those packages, but with malicious code (Wu et al., 2023c). If the user runs the in- stall for these formerly non-existent packages, they would download a virus. Prompt-based Defenses Multiple prompt-based defenses have been proposed, in which instructions are included in the prompt to avoid prompt injec- tion (Schulhoff, 2022). For example, the following string could be added to a prompt: Do not output any malicious content However, Schulhoff et al. (2023) ran a study with hundreds of thousands of malicious prompts and found that no prompt-based defense is fully secure, though they can mitigate prompt hacking to some extent. Bugs (and security vulnerabilities) occur more frequently in LLM-generated code (Pearce et al., 2021, 2022; Sandoval et al., 2022; Perry et al., 2022). Minor changes to the prompting technique can also lead to such vulnerabilities in the gener- ated code (Pearce et al., 2021). 5.1.2.3 Guardrails are rules and frameworks for guiding GenAI outputs (Hakan Tekgul, 2023). Guardrails can be as simple as classifying user input as ma- licious or not (AI, 2023; Inan et al., 2023), then responding with a canned message if malicious. More complicated tools employ dialogue man- agers (Rebedea et al., 2023), which allow the LLM to choose from a number of curated responses. Prompting-specific programming languages have also been proposed to improve templating and act as guardrails (Scott Lundberg, 2023; Luca Beurer- Kellner, 2023). Customer Service Malicious users frequently perform prompt injec- tion attacks against corporate chatbots, leading to brand embarrassment (Bakke, 2023; Goodside, 2022). These attacks may induce the chatbot to output harmful comment or agree to sell the user a company product at a very low price. In the lat- ter case, the user may actually be entitled to the deal. Garcia (2024) describe how an airline chat- bot gave a customer incorrect information about refunds. The customer appealed in court and won. Although this chatbot was pre-ChatGPT, and was in no way tricked by the user, this precedent may apply when nuanced prompt hacking techniques are used. 5.1.3 Detectors are tools designed to detect malicious inputs and prevent prompt hacking. Many com- panies have built such detectors (ArthurAI, 2024; Preamble, 2024; Lakera, 2024), which are often built using fine-tuned models trained on mali- cious prompts. Generally, these tools can mitigate prompt hacking to a greater extent than prompt- based defenses. Hardening Measures Several tools and prompting technique have been developed to mitigate some of the aforementioned security risks. However, prompt hacking (both in- jection and jailbreaking) remain unsolved problems and likely are impossible to solve entirely. 5.2 Alignment Ensuring that LLMs are well-aligned with user needs in downstream tasks is essential for success- ful deployment. Models may output harmful con- 29

30. Ambig. Demonstrations 5.2.4 Ambiguity 5.2.4 Question Clarification 5.2.4 AttrPrompt 5.2.3 Cultural Awareness 5.2.3 Biases 5.2.3 Demonstration Sel. 5.2.3 Alignment Vanilla Prompting 5.2.3 Sycophancy 5.2.2 Calibration 5.2.2 Verbalized Score 5.2.2 Few-Shot Ordering 5.2.1 Prompt Drift 5.2.1 Prompt Sensitivity 5.2.1 Prompt Wording 5.2.1 Task Format 5.2.1 Figure 5.2: Prompt-based Alignment Organization tent, yield inconsistent responses, or show bias, all of which makes deploying them more difficult. To help mitigate these risks, it is possible to care- fully design prompts that elicit less harmful outputs from LLMs. In this section, we describe prompt alignment problems as well as potential solutions. 5.2.1 and Hruschka, 2023; Zheng et al., 2023a). Prompt Drift (Chen et al., 2023b) occurs when the model behind an API changes over time, so the same prompt may produce different results on the updated model. Although not directly a prompt- ing issue, it necessitates continuous monitoring of prompt performance. Prompt Sensitivity Several works show that LLMs are highly sensitive to the input prompt (Leidinger et al., 2023), i.e., even subtle changes to a prompt such as exemplar order (Section 2.2.1.1) can result in vastly different outputs. Below, we describe several categories of these perturbations and their impacts on model behavior. 5.2.2 Overconfidence and Calibration LLMs are often overconfident in their answers, especially when prompted to express their own confidence in words (Kiesler and Schiffner, 2023; Xiong et al., 2023a), which may lead to user overreliance on model outputs (Si et al., 2023c). Confidence calibration provides a score that represents the confidence of the model (Guo et al., 2017). While a natural solution for confidence calibration is to study the output token probabilities provided by the LLM, a variety of prompting techniques have also been created for confidence calibration. Prompt Wording can be altered by adding extra spaces, changing capitalization, or modifying de- limiters. Despite these changes being minor, Sclar et al. (2023a) find that they can cause performance of LLaMA2-7B to range from nearly 0 to 0.804 on some tasks. Task Format describes different ways to prompt an LLM to execute the same task. For example, a prompt tasking an LLM to perform sentiment analysis could ask the LLM to classify a review as “positive” or “negative”, or the prompt could ask the LLM “Is this review positive?” to elicit a “yes” or “no” response. Zhao et al. (2021b) show that these minor changes can alter the accuracy of GPT-3 by up to 30%. Similarly, minor perturbations on task-specific prompts that are logically equivalent, such as altering the order of choices in multiple-choice questions, can result in significant performance degradation (Pezeshkpour Verbalized Score is a simple calibration tech- nique that generates a confidence score (e.g. “How confident are you from 1 to 10”), but its efficacy is under debate. Xiong et al. (2023b) find that several LLMs are highly overconfident when ver- balizing confidence scores, even when employing self-consistency and chain-of-thought. In contrast, Tian et al. (2023) find that simple prompts (Section 4.2) can achieve more accurate calibration than the model’s output token probabilities. Sycophancy refers to the concept that LLMs will often express agreement with the user, even when 30

31. that view contradicts the model’s own intial out- put. Sharma et al. (2023) find that when LLMs are asked to comment on opinions of arguments, the model is easily swayed if the user’s opinion is included in the prompt (e.g. “I really like/dis- like this argument”). Further, they find that ques- tioning the LLM’s original answer (e.g. “Are you sure?”), strongly providing an assessment of cor- rectness (e.g. “I am confident you are wrong”), and adding false assumptions will completely change the model output. Wei et al. (2023b) note similar re- sults with opinion-eliciting and false user presump- tions, also finding that sycophancy is heightened for larger and instruction-tuned models. Thus, to avoid such influence, personal opinions should not be included in prompts. 11 5.2.3 and styles. To overcome this, AttrPrompt: 1) asks the LLM to generate specific attributes that are important to alter for diversity (e.g. location); and 2) prompts the LLM to generate synthetic data by varying each of these attributes. 5.2.4 Ambiguity Questions that are ambiguous can be interpreted in multiple ways, where each interpretation could re- sult in a different answer (Min et al., 2020). Given these multiple interpretations, ambiguous questions are challenging for existing models (Keyvan and Huang, 2022), but a few prompting techniques have been developed to help address this challenge. Ambiguous Demonstrations Gao et al. (2023a) are examples that have an ambiguous label set. Including them in a prompt can increase ICL performance. This can be automated with a retriever, but it can also be done manually. Biases, Stereotypes, and Culture LLMs should be fair to all users, such that no bi- ases, stereotypes, or cultural harms are perpetuated in model outputs (Mehrabi et al., 2021). Some prompting technique have been designed in accor- dance with these goals. Question Clarification (Rao and Daumé III, 2019) allows the LLM to identify ambiguous ques- tions and generate clarifying questions to pose to the user. Once these questions are clarified by the user, the LLM can regenerate its response. Mu et al. (2023) do this for code generation and Zhang and Choi (2023) equip LLMs with a similar pipeline for resolving ambiguity for general tasks, but ex- plicitly design separate prompts to: 1) generate an initial answer 2) classify whether to generate clar- ification questions or return the initial answer 3) decide what clarification questions to generate 4) generate a final answer. Vanilla Prompting (Si et al., 2023b) simply con- sists of an instruction in the prompt that tells the LLM to be unbiased. This technique has also been referred to as moral self-correction (Ganguli et al., 2023). Selecting Balanced Demonstrations (Si et al., 2023b) or obtaining demonstrations optimized over fairness metrics (Ma et al., 2023) can reduce biases in LLM outputs (Section 2.2.1.1). Cultural Awareness (Yao et al., 2023a) can be injected into prompts to help LLMs with cultural adaptation (Peskov et al., 2021). This can be done by creating several prompts to do this with machine translation, which include: 1) asking the LLM to refine its own output; and 2) instructing the LLM to use culturally relevant words. AttrPrompt (Yu et al., 2023) is a prompting technique designed to avoid producing text biased towards certain attributes when generating syn- thetic data. Traditional data generation approaches may be biased towards specific lengths, locations 11 For example, a practitioner may use the prompt template “Detect all instances where the user’s input is harmful: {IN- PUT}” in an attempt to prevent adversarial inputs, but this subtly makes the false presupposition that the user’s input is actually harmful. Thus, due to sycophancy, the LLM may be inclined to classify the user’s output as harmful. 31

32. Benchmarking Now that we have carried out a systematic review of prompting techniques, we will analyze the em- pirical performance of different techniques in two ways: via a formal benchmark evaluation, and by illustrating in detail the process of prompt engineer- ing on a challenging real-world problem. Technique Benchmarking A formal evaluation of prompting technique might be done in a broad study that compares hundreds of them across hundreds of models and benchmarks. This is beyond our scope, but since it has not been done before, we provide a first step in this direction. We choose a subset of prompting techniques and run them on the widely used benchmark MMLU (Hendrycks et al., 2021). We ran on a representa- tive subset of 2,800 MMLU questions (20% of the questions from each category). 12 and used gpt-3.5- turbo for all experiments. 1.0 0.8 Comparing Prompting Techniques We benchmark six distinct prompting techniques using the same general prompt template (Figure 6.2). This template shows the location of different components of the prompts. Only base instructions and question exist in every prompt. The base in- struction is a phrase like "Solve the problem and return (A), (B), (C) or (D)." that we vary in some cases. We additionally test two formats of the ques- tion (Figures 6.3 and 6.4). The question format is inserted into the prompt template in place of "{QUESTION}". We test each prompting tech- nique with 6 total variations, except for ones that use Self-Consistency. 0.6 0.4 0.2 Co 0.627 0.547 0.574 0.652 0.692 0.691 0.0 6.1.1 Few-Shot Techniques We also ran Few-Shot prompts and Few-Shot-CoT prompts, both with exemplars generated by one of our authors. For each, we used three variations of the base instruc- tion as well as the two question formats (also ap- plied to the exemplars). Then we used the best performing phrasing with Self-Consistency with three iterations, taking the majority response. 6.1 we used three thought inducers (instructions that cause the model to generate reasoning steps) includ- ing the standard "Let’s think step by step" chain- of-thought (Kojima et al., 2022), as well as ThoT (Zhou et al., 2023) and Plan and Solve (Wang et al., 2023f). Then, we selected the best of these, then ran it with Self-Consistency with three iterations, taking the majority response. 6 Figure 6.1: Accuracy values are shown for each prompt- ing technique. Purple error bars illustrate the minimum and maximum for each technique, since they were each run on different phrasings and formats (except SC). 6.1.2 Question Formats Zero-Shot As a baseline, we ran questions di- rectly through the model without any prompting techniques. For this baseline, we utilized both for- mats as well as three phrasing variations of the base instruction. Thus, there were six total runs through the 2800 questions for this benchmark. This did not include any exemplars or thought inducers. We experiment with two formatting choices from Sclar et al. (2023b), who explored how formatting choices can affect benchmarking results. We use two formats which lead to varied results on their task (Figures 6.3 and 6.4). Zero-Shot-CoT Techniques We ran also ran Zero-Shot-CoT. As the three different variations, For the two Self-Consistency results, we set temper- ature to 0.5, following Wang et al. (2022)’s guide- lines. For all other prompts, a temperature of 0 was used. 6.1.3 12 We excluded human_sexuality, since gpt-3.5-turbo re- fused to answer these questions. 32 Self-Consistency

33. {BASE_INSTRUCTION} {EXEMPLARS} {QUESTION} {THOUGHT_INDUCER} PROBLEM::{QUESTION}, OPTIONS:: (A): {A} (B): {B} (C): {C} (D): {D}, ANSWER:: Figure 6.2: Prompt template for benchmarking. Figure 6.4: Question format 2. Problem {QUESTION} Options of actually solving the problem. Rather, it provides one illustration of how an experienced prompt en- gineer would approach a task like this, along with lessons learned. (A)::{A} (B)::{B} (C)::{C} (D)::{D} Answer 6.2.1 Figure 6.3: Question format 1. 6.1.4 Our illustrative problem involves detection of sig- nal that is predictive of crisis-level suicide risk in text written by a potentially suicidal individual. Sui- cide is a severe problem worldwide, compounded, as are most mental health issues, by a desperate lack of mental health resources. In the United States, more than half the national population lives in federally defined mental heath provider short- age areas (National Center for Health Workforce Analysis, 2023); in addition, many mental health professionals lack core competencies in suicide prevention (Cramer et al., 2023). In 2021, 12.3M Americans thought seriously about suicide, with 1.7M actually making attempts resulting in over 48,000 deaths (CDC, 2023). In the U.S., suicide was the second leading cause of death (after acci- dents) in people aged 10-14, 15-24, or 25-34 as of 2021 statistics, and it was the fifth leading cause of death in people aged 35–54 (Garnett and Curtin, 2023). Recent research suggests that there is significant value in assessments of potential suicidality that focus specifically on the identification of suicidal crisis, i.e. the state of acute distress associated with a high risk of imminent suicidal behavior. However, validated assessments for diagnostic approaches such as Suicide Crisis Syndrome (SCS) (Schuck et al., 2019b; Melzer et al., 2024) and Acute Sui- cidal Affective Disturbance (Rogers et al., 2019) require either personal clinical interactions or com- pletion of self-report questionnaires that contain dozens of questions. The ability to accurately flag indicators of suicidal crisis in individuals’ language could therefore have a large impact within the men- tal health ecosystem, not as a replacement for clini- Evaluating Responses Evaluating whether a LLM has properly responded to a question is a difficult task (Section 2.5). We marked answers as correct if they followed certain identifiable patterns, such as being the only capital- ized letter (A-D) within parentheses or following a phrase like “The correct answer is”. 6.1.5 Results Performance generally improved as techniques grew more complex (Figure 6.1). However, Zero- Shot-CoT dropped precipitously from Zero-Shot. Although it had a wide spread, for all variants, Zero-Shot performed better. Both cases of Self- Consistency, naturally had lower spread since they repeated a single technique, but it only improved ac- curacy for Zero-Shot prompts. Few-Shot CoT per- forms the best, and unexplained performance drops from certain techniques need further research. As prompting technique selection is akin to hyperpa- rameter search, this it is a very difficult task (Khat- tab et al., 2023). However, we hope this small study spurs research in the direction of more performant and robust prompting techniques. 6.2 Problem Prompt Engineering Case Study Prompt engineering is emerging as an art that many people have begun to practice professionally, but the literature does not yet include detailed guid- ance on the process. As a first step in this direction, we present an annotated prompt engineering case study for a difficult real-world problem. This is not intended to be an empirical contribution in terms 33

34. cal judgment but as a way to complement existing practices (Resnik et al., 2021). As a starting point, we focus here on the most important predictive factor in Suicide Crisis Syn- drome assessments, referred to in the literature as either frantic hopelessness or entrapment, “a desire to escape from an unbearable situation, tied with the perception that all escape routes are blocked” (Melzer et al., 2024). 13 This characteristic of what an individual is experiencing is also central in other characterizations of mental processes that result in suicide. 6.2.2 exercise proceeded through 47 recorded develop- ment steps, cumulatively about 20 hours of work. From a cold start with 0% performance (the prompt wouldn’t return properly structured responses), per- formance was boosted to an F1 of 0.53, where that F1 is the harmonic mean of 0.86 precision and 0.38 recall. 15 Below, the set of prompts q inf is the test item, while q i , r i , and a i denote the questions, chain-of- thought steps, and answers in exemplars. 6.2.3.1 The process began with the prompt engineer review- ing a description of entrapment (Figure 6.7); this description had been used as a first-pass rubric for the human coders early in the coding process, not- ing, however, that they were familiar with SCS and knew it was neither a formal definition nor exhaus- tive. The prompt engineer then loaded the dataset into a Python notebook for data exploration pur- poses. He began by asking gpt-4-turbo-preview if it knew what entrapment was (Figure 6.8), but found that the LLM’s response was not similar to the de- scription that had been given. In consequence, the prompt engineer included the Figure 6.7 descrip- tion of entrapment in all future prompts. The Dataset We worked with a subset of data from the Univer- sity of Maryland Reddit Suicidality Dataset (Shing et al., 2018), which is constructed from posts in r/SuicideWatch, a subreddit that offers peer sup- port for anyone struggling with suicidal thoughts. Two coders trained on the recognition of the factors in Suicide Crisis Syndrome coded a set of 221 posts for presence or absence of entrapment, achieving solid inter-coder reliability (Krippendorff’s alpha = 0.72). 6.2.3 Dataset Exploration (2 steps) The Process An expert prompt engineer, who has authored a widely used guide on prompting (Schulhoff, 2022), took on the task of using an LLM to identify entrap- ment in posts. 14 The prompt engineer was given a brief verbal and written summary of Suicide Crisis Syndrome and entrapment, along with 121 develop- ment posts and their positive/negative labels (where “positive” means entrapment is present), the other 100 labeled posts being reserved for testing. This limited information mirrors frequent real-life sce- narios in which prompts are developed based on a task description and the data. More generally, it is consistent with a tendency in natural language processing and AI more generally to approach cod- ing (annotation) as a labeling task without delving very deeply into the fact that the labels may, in fact, refer to nuanced and complex underlying social science constructs. We documented the prompt engineering pro- cess in order to illustrate the way that an experi- enced prompt engineer goes about their work. The 6.2.3.2 Getting a Label (8 steps) As noted in Section 6.1 with regard to the hu- man_sexuality subset of MMLU, LLMs exhibit unpredictable and difficult to control behaviour in sensitive domains. For multiple steps in the prompt engineering process, the prompt engineer found that the LLM was giving mental health advice (e.g. Figure 6.9) instead of labeling the input. This was addressed by switching to the GPT-4-32K model. A take-away from this initial phase is that the “guard rails” associated with some large language models may interfere with the ability to make progress on a prompting task, and this could in- fluence the choice of model for reasons other than the LLM’s potential quality. 6.2.3.3 Prompting Techniques (32 steps) The prompt engineer then spent the majority of his time improving the prompting technique being used. This included techniques such as Few-Shot, 13 The former term more explicitly emphasizes the frantic and desperate action required to escape an unbearable life situation. However, the term entrapment is briefer and used widely so we adopt it here. 14 Disclosure: that expert is also the lead author of this paper. 15 Precision is also known as positive predictive value, and recall is also known as true positive rate or sensitivity. Al- though F1 is often used in computional system evaluations as a single figure of merit, we note that in this problem space its even weighting of precision and recall is probably not appropriate. We discuss this further below. 34

35. Scores of Different Prompting Techniques on Development Set 1.0 F1 Recall Precision 0.8 0.6 0.4 + Triplicate 10-Shot AutoDiCoT De-Dupe Context 0.0 0.2 Figure 6.5: F1 scores varied widely from worst performing prompts to highest performing prompts, but most prompts scored within a similar range. Chain-of-Thought, AutoCoT, Contrastive CoT, and multiple answer extraction techniques. We report statistics for the first runs of these techniques; F1 scores could change by as much as 0.04 upon sub- sequent runs, even with temperature and top p set to zero. 16 section until we reach CoT. This approach obtained a 0.25 recall, 1.0 precision, and 0.40 F1, evaluated on all samples from the training/development since no samples had been used as exemplars. 10-Shot + Context. Next, the prompt engineer added the first ten data samples (with labels) into the prompt, in Q: (question) A: (answer) format (Figure 6.11). This 10-shot prompt was evaluated on the remaining items in the training/development set, yielding ↑ 0.05 (0.30) recall, ↓ 0.70 (0.30) pre- cision, and ↑0.05 (0.45) F1 relative to the previous best prompt. 17 Zero-Shot + Context was the first technique eval- uated (Figure 6.10), using the description in Fig- ure 6.7. Notice the word definition in the prompt, although Figure 6.7 is not a formal definition. In order to obtain a final response from the LLM to use in calculating performance metrics, it was necessary to extract a label from the LLM output. The prompt engineer tested two extractors, one that checks if the output is exactly "Yes" or "No", and another which just checks if those words match the first few characters of the output. The latter had better performance, and it is used for the rest of this One-Shot AutoDiCot + Full Context. After per- forming 10-shot prompting, the prompt engineer observed that the 12th item in the development set was being incorrectly being labeled as a positive in- stance, and began experimenting with ways of mod- 17 Here and for the remainder of the case study, we judge “best” by F1, and we report on the current prompt under dis- cussion relative to the best performing previous prompt. 16 Temperature and top-p are configuration hyperparameters that control randomness of the output (Schulhoff, 2022). 35

36. 0.0 Techniques 36 Anonymized Context F1 Scores of Prompting Techniques on Development Set 0.5 Max F1 Score: 0.53 0.4 0.3 0.2 0.1 Figure 6.6: From the first prompt tried (Zero-Shot + Context) to the last (Anonymized Email), improvements in F1 score were hard to come by and and often involved testing multiple underperforming prompts before finding a performant one. Green lines show improvements over the current highest F1 score, while red lines show deteriorations.

37. Entrapment: - Feeling like there is no exit - Feeling hopeless - Feeling like there is no way out - Feeling afraid that things will never be normal again - Feeling helpless to change - Feeling trapped - Feeling doomed - Feeling or thinking that things will never change - Feeling like there is no escape - Feeling like there are no good solutions to problems {ENTRAPMENT DEFINITION (Figure 6.7)} {q inf } Is this entrapment? Yes or no. Figure 6.10: A Zero-Shot + Context prompt, the sim- plest of all prompts explored in this case study. {ENTRAPMENT DEFINITION (Figure 6.7)} Q: {q 1 } A: {a 1 } ... Q: {q 10 } A: {a 10 } Q: {q inf } A: Figure 6.7: The description of entrapment used by the prompt engineer What is entrapment with respect to Suicide Crisis Syndrome? Figure 6.11: 10-Shot + Context Prompt Figure 6.12 shows a version of that process, gen- eralized to produce explanations for all develop- ment question/answer items (q i , a i ) in a set T rather than just item 12. Informed by the reasoning steps r 12 elicited with respect to the incorrectly labeled q 12 , the previous prompt was modified by including r 12 in a One-Shot CoT example with incorrect rea- soning, as an exemplar for what not to do (Figure 6.13). We call the algorithm in Figure 6.12 Automatic Directed CoT (AutoDiCoT), since it automatically directs the CoT process to reason in a particular way. This technique can be generalized to any labeling task. It combines the automatic generation of CoTs (Zhang et al., 2022b) with showing the LLM examples of bad reasoning, as in the case of Contrastive CoT (Chia et al., 2023). The algorithm was also used in developing later prompts. Finally, the prompt was extended with two ad- ditional pieces of context/instruction. The first was an email message the prompt engineer had received explaining overall goals of the project, which provided more context around the concept of entrapment and the reasons for wanting to label it. The second addition was inspired by the prompt engineer noticing the model was frequently over- generating a positive label for entrapment. Hypoth- esizing that the model was being too aggressive in its pretraining-based inferences from the overt Figure 6.8: Question asked to the LLM to determine whether its training data had provided relevant knowl- edge about entrapment (it had not). ifying the prompting such that the model would get that item correct. In order to get a sense of why this mislabeling was taking place, the prompt engineer prompted the LLM to generate an explanation of why the 12th item would have been labeled the way it was. 18 18 We are trying to avoid misleading language like “the LLM generated an explanation of its reasoning”. LLMs do not have access to their own internal processes, and therefore they cannot “explain their reasoning” in the usual sense. An LLM generating an “explanation” is producing description of potential reasoning steps in getting to the output that could be true, but also may not be accurate at all. If you’re in immediate danger of harming yourself, please contact emergency services or a crisis hotline in your area. They can provide immediate support and help ensure your safety. Figure 6.9: A snippet from an output, which does not la- bel the data point, but rather attempts to provide mental health support to the user. Such outputs are often five times as long as this snippet. 37

38. {PROFESSOR’S EMAIL} 1. Require: Development items T with n pairs (q i , a i ) {ENTRAPMENT DEFINITION (Figure 6.7)} 2. For each pair (q i , a i ) in T : (a) Label q i as entrapment or not en- trapment using the model (b) If the model labels correctly: i. Prompt the model with "Why?" to generate a reason- ing chain r i (c) Else: i. Prompt the model with "It is actually [is/is not] entrap- ment, please explain why." to generate a reasoning chain r i (d) Store the tuple (q i , r i , a i ) IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped. Q: {q 12 } R: Although "Today I found out I have 10 days to vacate my apartment or I’ll be formally evicted. I’m 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I’ll probably be homeless" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment. seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment. A: {a 12 } Q: {q inf } 3. Return: n tuples (q i , r i , a i ) Figure 6.12: Algorithm: Automatic Directed CoT language, he instructed the model to restrict itself to explicit statements of entrapment (Figure 6.13). Below we refer to these two pieces of context, pro- vided in addition to the description of entrapment, as full context. A new extractor was also used for this prompt, which checks if the last word in the output is "Yes" or "No", instead of the first word. This updated prompt was tested against all inputs in the develop- ment set except for the first 20. It did not improve F1, ↓0.09 (0.36) F1, but it led the prompt engineer in a direction that did, as discussed below. Preci- sion improved to ↑ 0.09 (0.39) precision and recall dropped ↑ 0.03 (0.33) recall. At this point, though, it is worth observing that, although it did ultimately lead to a gain in F1 score, the steps taken here to cut down on over-generation of positive labels were not, in fact, the right move in terms of the longer term goals. Entrapment need not be expressed explicitly in order to be present (e.g. through phrases like “I feel trapped” or “There’s no way out”); rather, clinical experts who have looked at the texts found that expressions of entrapment could be implicit and potentially quite nuanced. Moreover, in most use cases for automatically spotting entrapment in someone’s language, precision and recall are unlikely to be equally important and, of the two, the recall/sen- Figure 6.13: One-Shot AutoDiCot + Full Context sitivity (i.e. not missing people who should be flagged as at-risk) may matter more because the potential cost of a false negative is so high. The take-away here, although the insight came later, is that it is easy for the process of prompt development to diverge from the actual goals un- less regular engagement is fostered between the prompt engineer and domain experts who more deeply understand the real-world use case. Ablating Email. The results of the previous changes were promising, but they did involve cre- ating a prompt that included information from an email message that had not been created for that purpose, and which included information about the project, the dataset, etc. that were not intended for disclosure to a broad audience. Ironically, though, removing this email significantly brought perfor- mance back down, ↓ 0.18 (0.18) F1, ↓ 0.22(0.17) precision and ↓ 0.13 (0.20) recall. We attribute this to the fact that the email provided richer back- ground information about the goals of the label- 38

39. {PROFESSOR’s EMAIL} {ENTRAPMENT ure 6.7)} DEFINITION {PROFESSOR’s EMAIL} {PROFESSOR’s EMAIL} (Fig- {ENTRAPMENT ure 6.7)} IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped. DEFINITION (Fig- IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped. Q: {q 1 } A: {a 1 } ... Q: {q 10 } A: {a 10 } Q: {q 12 } R: Although "{LLM REASONING}" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment. A: {a 12 } Q: {q inf } Q: {q inf } A: Figure 6.15: Full Context Only {PROFESSOR’s EMAIL} {ENTRAPMENT DEFINITION} IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped. Figure 6.14: 10-Shot + 1 AutoDiCoT Q: {q 1 } R: {r 1 } A: {a 1 } ... Q: {q 10 } R: {r 10 } A: {a 10 } Q: {q inf } ing. Although we would not recommend including email or any other potentially identifying informa- tion in any LLM prompt, we chose to leave the email in the prompt; this is consistent with scenar- ios in many typical settings, in which prompts are not expected to be exposed to others. 10-Shot + 1 AutoDiCoT. As a next step, the prompt engineer tried including full context, 10 reg- ular exemplars, and the one-shot exemplar about how not to reason. This hurt performance (Figure 6.14) ↓ 0.30 (0.15) F1, ↓ 0.15 (0.15) precision, ↓ 0.15 (0.15) recall. Figure 6.16: 10-Shot AutoDiCoT how improvements can arise through exploration and fortuitous discovery. On the pessimistic side, the value of duplicating the email in the prompt highlights the extent to which prompting remains a difficult to explain black art, where the LLM may turn out to be unexpectedly sensitive to variations one might not expect to matter. Full Context Only. Next, a prompt was created using only full context, without any exemplars (Fig- ure 6.15). This boosted performance over the pre- vious technique, ↓ 0.01 (0.44) F1, ↓ 0.01 (0.29) precision, ↑ 0.62 (0.92) recall. Interestingly, in this prompt, the prompt engineer accidentally pasted in the full-context email twice, and that ended up hav- ing significant positive effects on performance later (and removing the duplicate actually decreased per- formance). This is reminiscent of the re-reading technique (Xu et al., 2023). This can be interpreted both optimistically and pessimistically. Optimistically, it demonstrates 10-Shot AutoDiCoT. The next step was to create more AutoDiCoT exemplars, per the algorithm in Figure 6.12. A total of ten new AutoDiCoT exem- plars were added to the full context prompt (Figure 6.16). This yielded the most successful prompt from this prompt engineering exercise, in terms of F1 score, ↑ 0.08 (0.53) F1, ↑ 0.08 (0.38) precision, ↑ 0.53 (0.86) recall. 39

40. {PROFESSOR’s EMAIL} {PROFESSOR’s EMAIL} {ENTRAPMENT DEFINITION} {ENTRAPMENT DEFINITION} IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped. IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped. Question: {q 1 } Reasoning: {r 1 } Answer: {a 1 } ... Question: {q 20 } Reasoning: {r 20 } Answer: {a 20 } Question: {q inf } Question: {REDACTED} Answer: {ANSWER} Does this Answer indicate entrapment? Output the word Yes if it is labeled as entrapment and output the word No if it is not labeled as entrapment. Only output the word Yes or the word No. Figure 6.18: Extraction Prompt Figure 6.17: 20-shot AutoDiCoT 10-Shot AutoDiCoT + Extraction Prompt. Ap- plying the extraction prompt to the best performing 10-Shot AutoDiCoT prompt did not improve re- sults, ↓ 0.04 (0.49) F1, ↓ 0.06 (0.78) recall, ↓ 0.03 (0.35) precision. 20-Shot AutoDiCoT. Further experimentation proceeded seeking (unsuccesfully) to improve on the previous F1 result. In one attempt, the prompt engineer labeled an additional ten exemplars, and created a 20-shot prompt from the first 20 data points in the development set. This led to worse results than the 10-shot prompt, when tested on all samples other than the first twenty, ↓ 0.04 (0.49) F1, ↓ 0.05 (0.33) precision, ↑ 0.08 (0.94) recall. Notably, it also yielded worse performance on the test set. 10-Shot AutoDiCoT without Email. As noted above, removing the email outright from the prompt hurt performance, ↓ 0.14 (0.39) F1, ↓ 0.38 (0.48) recall, ↓ 0.05 (0.33) precision. De-Duplicating Email. Also as noted above, it seemed reasonable that removing the duplication of the email would perform as well or better than the prompt with the unintentional duplication. As it turned out, however, removing the duplicate signif- icantly hurt performance, ↓ 0.07 (0.45) F1, ↓ 0.13 (0.73) recall, ↓ 0.05 (0.33) precision. 20-Shot AutoDiCoT + Full Words. The prompt engineer conjectured that the LLM would perform better if the prompt included full words Question, Reasoning, and Answer rather than Q, R, A. How- ever, this did not succeed (Figure 6.17), ↓ 0.05 (0.48) F1, ↓ 0.06 (0.32) precision, ↑ 0.08 (0.94) recall. 10-Shot AutoDiCoT + Default to Negative. This approach used the best performing prompt, and de- faulted to labeling as negative (not entrapment) in the case of answers that are not extracted properly. This did not help performance, ↓ 0.11 (0.42) F1, ↓ 0.03 (0.83) recall, ↓ 0.10 (0.28) precision. 20-Shot AutoDiCoT + Full Words + Extraction Prompt. The prompt engineer then noticed that in many cases, the LLM generated outputs that could not properly be parsed to obtain a response. So, they crafted a prompt that extracted answers from the LLM’s response (Figure 6.18). Although this improved accuracy by a few points, it decreased F1, thanks to the fact that many of the outputs that had been unparsed actually contained incorrect responses, ↓ 0.05 (0.48) F1, ↓ 0.05 (0.33) precision, with no change in recall (0.86). Ensemble + Extraction. Especially for systems that are sensitive to the details of their inputs, there are advantages in trying multiple variations of an input and then combining their results. That was done here by taking the best performing prompt, the 10-Shot AutoDiCoT prompt, and creating three versions of it with different orderings of the exem- 40

41. Scores of Different Prompting Techniques on Test Set plars. The average of the three results was taken to be the final answer. Unfortunately, both orderings that differed from the default ordering led to the LLM not outputting a well structured response. An extraction prompt was therefore used to obtain final answers. This exploration hurt rather than helped performance ↓ 0.16 (0.36) F1, ↓ 0.22 (0.64) recall, ↓ 0.12 (0.26) precision. 0.8 F1 Recall Precision 0.6 0.4 10-Shot AutoCoT + 3x the context (no email dupe). Recall that context refers to the descrip- tion of entrapment, an instruction about explicit- ness, and an email. Since the duplicated email had improved performance, the prompt engineer tested out pasting in three copies of the context (first de-duplicating the email). However, this did not improve performance, ↓ 0.06 (0.47) F1, ↓ 0.08 (0.78) recall, ↓ 0.05 (0.33) precision. 0.0 0.2 Figure 6.19: Scores of different prompting techniques on the test set. instruction about the explicitness of entrapment. It also performs much better than the human prompt engineer’s prompts on the test set, which demon- strates the significant promise of automated prompt engineering. Anonymize Email. At this point it seemed clear that including the duplicated email in the prompt was actually, although not explainably, essential to the best performance so far obtained. The prompt engineer decided to anonymize the email by re- placing personal names with other, random names. However, surprisingly, this decreased performance significantly ↓ 0.08 (0.45) F1, ↓ 0.14 (0.72) recall, ↓ 0.05 (0.33) precision. 6.2.4 Discussion Prompt engineering is a non-trivial process, the nu- ances of which are not currently well described in literature. From the fully manual process illustrated above, there are several take-aways worth summa- rizing. First, prompt engineering is fundamentally different from other ways of getting a computer to behave the way you want it to: these systems are being cajoled, not programmed, and, in addition to being quite sensitive to the specific LLM being used, they can be incredibly sensitive to specific details in prompts without there being any obvi- ous reason those details should matter. Second, therefore, it is important to dig into the data (e.g. generating potential explanations for LLM “reason- ing” that leads to incorrect responses). Related, the third and most important take-away is that prompt engineering should involve engagement between the prompt engineer, who has expertise in how to coax LLMs to behave in desired ways, and domain experts, who understand what those desired ways are and why. Ultimately we found that there was significant promise in an automated method for exploring the prompting space, but also that combining that au- tomation with human prompt engineering/revision was the most successful approach. We hope that this study will serve as a step toward more robust examinations of how to perform prompt engineer- ing. DSPy. We concluded the case study by explor- ing an alternative to manual prompt engineer- ing, the DSPy framework (Khattab et al., 2023), which automatically optimizes LLM prompts for a given target metric. Specifically, we begin with a chain-of-thought classification pipeline that uses the definition of entrapment in Figure 6.7. Over 16 iterations, DSPy bootstrapped syn- thetic LLM-generated demonstrations and ran- domly sampled training exemplars, with the ultimate objective of maximizing F 1 on the same development set used above. We used gpt-4-0125-preview and the default settings for the BootstrapFewShotWithRandomSearch “teleprompter” (the optimization approach). Fig- ure 6.19 shows the results of two of these prompts on the test set, one of which used default DSPy behaviour, and the second which was manually modified slightly from this default. The best result- ing prompt includes 15 exemplars (without CoT reasoning) and one bootstrapped reasoning demon- stration. It achieves 0.548 F 1 (and 0.385 / 0.952 precision / recall) on the test set, without making any use of the professor’s email nor the incorrect 41

42. 7 Related Work In this section, we review existing surveys and meta-analyses of prompting. Liu et al. (2023b) perform a systematic review of prompt engineer- ing in the pre-ChatGPT era, including various aspects of prompting like prompt template engi- neering, answer engineering, prompt ensembling, and prompt tuning methods. Their review cov- ers many different types of prompting (e.g., cloze, soft-prompting, etc., across many different types of language models) while we focus on discrete pre-fix prompting but more in-depth discussion. Chen et al. (2023a) provide a review of popular prompting techniques like Chain-of-Thought, Tree- of-Thought, Self-Consistency, and Least-to-Most prompting, along with outlooks for future prompt- ing research. White et al. (2023) and Schmidt et al. (2023) provide a taxonomy of prompt pat- terns, which are similar to software patterns (and prompting techniques for that matter). Gao (2023) provide a practical prompting technique tutorial for a non-technical audience. Santu and Feng (2023) provide a general taxonomy of prompts that can be used to design prompts with specific properties to perform a wide range of complex tasks. Bubeck et al. (2023) qualitatively experiment with a wide range of prompting methods on the early version of GPT-4 to understand its capabilities. Chu et al. (2023) review Chain-of-Thought related prompt- ing methods for reasoning. In earlier work, Bom- masani et al. (2021) review and discuss opportuni- ties and risks of foundation models broadly, and Dang et al. (2022) discuss prompting strategies for interactive creative applications that use prompting as a new paradigm for human interaction, with a particular focus on the user interface design that supports user prompting. As an addition to these existing surveys, our review aims to provide a more updated and formalized systematic review. erage. Hua et al. (2024) use a GPT-4-automated ap- proach to review LLMs in the mental health space. Wang et al. (2023c) review prompt engineering and relevant models in the visual modality and Yang et al. (2023e) provided a comprehensive list of qual- itative analyses of multimodal prompting, particu- larly focusing on GPT-4V 19 . Durante et al. (2024) review multimodal interactions based on LLM em- bodied agents. Ko et al. (2023b) review literature on the adoption of Text-to-Image generation mod- els for visual artists’ creative works. Gupta et al. (2024) review GenAI through a topic modeling approach. Awais et al. (2023) review foundation models in vision, including various prompting tech- niques. Hou et al. (2023) perform a systematic review of prompt engineering techniques as they relate to software engineering. They use a sys- tematic review technique developed by Keele et al. (2007), specifically for software engineering re- views. Wang et al. (2023e) review the literature on software testing with large language models. Zhang et al. (2023a) review ChatGPT prompting performance on software engineering tasks such as automated program repair. Neagu (2023) provide a systematic review on how prompt engineering can be leveraged in computer science education. Li et al. (2023j) review literature on the fairness of large language models. There are also surveys on related aspects such as hallucination of language models (Huang et al., 2023b), verifiability (Liu et al., 2023a), reasoning (Qiao et al., 2022), aug- mentation (Mialon et al., 2023), and linguistic prop- erties of prompts (Leidinger et al., 2023). Different from these works, we perform our review targeting broad coverage and generally applicable prompt- ing techniques. Finally, in terms of more general prior surveys (Liu et al., 2023b; Sahoo et al., 2024), this survey offers an update in a fast-moving field. In addition, we provide a starting point for taxo- nomic organization of prompting techniques and standardization of terminology. Moreover, we base our work in the widely well-received standard for systematic literature reviews — PRISMA (Page et al., 2021). There is also a line of work that surveys prompt- ing techniques for particular domains or down- stream applications. Meskó (2023) and Wang et al. (2023d) offer recommended use cases and limi- tations of prompt engineering in the medical and healthcare domains. Heston and Khun (2023) pro- vide a review of prompt engineering for medical education use cases. Peskoff and Stewart (2023) query ChatGPT and YouChat to assess domain cov- 19 https://openai.com/research/ gpt-4v-system-card 42

43. 8 Conclusions Generative AI is a novel technology, and broader understanding of models’ capabilities and limita- tions remains limited. Natural language is a flexi- ble, open-ended interface, with models having few obvious affordances. The use of Generative AI therefore inherits many of the standard challenges of linguistic communication—e.g., ambiguity, the role of context, the need for course correction— while at the same time adding the challenge of communicating with an entity whose “understand- ing” of language may not bear any substantial re- lationship to human understanding. Many of the techniques described here have been called “emer- gent”, but it is perhaps more appropriate to say that they were discovered—the result of thorough ex- perimentation, analogies from human reasoning, or pure serendipity. The present work is an initial attempt to catego- rize the species of an unfamiliar territory. While we make every attempt to be comprehensive, there are sure to be gaps and redundancies. Our inten- tion is to provide a taxonomy and terminology that cover a large number of existing prompt engineer- ing techniques, and which can accommodate future methods. We discuss over 200 prompting tech- niques, frameworks built around them, and issues like safety and security that need to be kept in mind when using them. We also present two case studies in order to provide a clear sense of models’ ca- pabilities and what it is like to tackle a problem in practice. Last, our stance is primarily observa- tional, and we make no claims to the validity of the presented techniques. The field is new, and evalua- tion is variable and unstandardized—even the most meticulous experimentation may suffer from unan- ticipated shortcomings, and model outputs them- selves are sensitive to meaning-preserving changes in inputs. As a result, we encourage the reader to avoid taking any claims at face value and to rec- ognize that techniques may not transfer to other models, problems, or datasets. To those just beginning in prompt engineering, our recommendations resemble what one would recommend in any machine learning setting: un- derstand the problem you are trying to solve (rather than just focusing on input/output and benchmark scores), and ensure the data and metrics you are working with constitute a good representation of that problem. It is better to start with simpler ap- proaches first, and to remain skeptical of claims about method performance. To those already en- gaged in prompt engineering, we hope that our tax- onomy will shed light on the relationships between existing techniques. To those developing new tech- niques, we encourage situating new methods within our taxonomy, as well as including ecologically valid case studies and illustrations of those tech- niques. Acknowledgements We appreciate the advice given by Hal Daumé III, Adam Visokay, and Jordan Boyd-Graber and review by Diyi Yang and Brandon M. Stewart. We also appreciate the 10K USD in API credits given by OpenAI and design work by Benjamin DiMarco. 43

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59. A A.1 Appendices Definitions of Prompting Reference (Meskó, 2023) (Chen et al., 2023a) (Santu and Feng, 2023) (Wang et al., 2023d) (Wang et al., 2023c) (Hou et al., 2023) (Wang et al., 2023e) (White et al., 2023) Prompt Prompt Engineering The practice of designing, refining, and implementing prompts or instructions that guide the output of LLMs to help in vari- ous tasks. It is essentially the practice of effectively interacting with AI systems to optimize their benefits. the input of the model the process of structuring input text for LLMs and is a technique integral to opti- mizing the efficacy of LLMs refers to a textual input provided to the involves crafting and revising the query LLMs with the intention of guiding its or context in such a way that it elicits the output toward a specific task desired response or behavior from LLMs involves designing effective prompts to guide the pre-trained language model in downstream tasks. the process of designing prompts that en- able the model to adapt and generalize to different tasks. downstream tasks. manually predefined natural language in- the careful design of specialized prompts structions input of the LLMs communicate with LLMs to steer its be- havior for desired outcomes Instructions given to an LLM to enforce an increasingly important skill set needed rules, automate processes, and ensure spe- to converse effectively with large lan- cific qualities (and quantities) of generated guage models (LLMs), such as ChatGPT output. Prompts are also a form of pro- the means by which LLMs are pro- gramming that can customize the outputs grammed via prompts and interactions with an LLM. A prompt is a set of instructions provided to an LLM that programs the LLM by cus- tomizing it and/or en- hancing or refining its capabilities (Heston and the input structuring the input in a specialized man- Khun, 2023) ner (Liu et al., choosing a proper prompt 2023b) the process of creating a prompting func- tion f prompt (x) that results in the most effective performance on the downstream task. 59

60. (Hadi et al., 2023) (Neagu, 2023) (Dang et al., 2022) the instructions provided to an LLM to refers to the designing and wording of make it follow specified rules, automation prompts given to LLMs so as to get a de- of processes and to ensure that the out- sired response from them. put generated is of a specific quality or quantity entails various strate- gies, including ex- plicit instruction, and implicit context [21]. Explicit instruction involves providing ex- plicit guidance or constraints to the model through instructions, examples, or speci- fications. Implicit context leverages the model’s under- standing of the preceding context to influence its response the systematic practice of constructing prompts to improve the generated output of a generative model Table A.1: Definitions of Prompt and Prompt Engineering from different papers. 60

61. A.2 A.2.1 Extended Vocabulary Prompting Terms Context Window The context window is the space of tokens (for LLMs) which the model can process. It has a maximal length (the context length). Priming (Schulhoff, 2022) refers to giving a model an initial prompt that lays out certain instructions for the rest of a conversation. This priming prompt might contains a role or other instructions on how to interact with the user. Priming can either be done in the system or user prompt (see below). A.2.2 Prompt Engineering Terms Conversational Prompt Engineering is Prompt Engineering in colloquio. That is, during the course of a conversation with a GenAI, a user may ask the GenAI to refine its output. In contrast, prompt engineering is often done by sending the GenAI a completely new prompt rather than continuing a conversation. A.2.3 Fine-Tuning Terms Prompt-Based Learning (Liu et al., 2023b), also known as Prompt Learning (Liu et al., 2023b; Wang et al., 2023d) refers to the process of using prompting-related techniques. It often is used in the context of fine-tuning, especially fine-tuning prompts. Due to conflicting usage, we do not use this term. Prompt Tuning (Lester et al., 2021) refers to directly optimizing the weights of the prompt itself, usually through some form of gradient-based updates. It has also been referred to has Prompt Fine-Tuning. It should not be used to refer to discrete prompt engineering. A.2.4 Orthogonal Prompt Types We now discuss terminology for high-level ways of classifying prompts. A.2.4.1 Originator User Prompt This is the type of prompt that comes from the user. This is the most common form of prompting and is how prompts are usually delivered in consumer applications. Assistant Prompt This "prompt" is simply the output of the LLM itself. It can be considered a prompt (or part of one) when it is fed back into the model, for example as part of a conversation history with a user. System Prompt This prompt is used to give LLMs high level instructions for interacting with users. Not all models have this. A.2.4.2 Hard vs Soft Prompts Hard (discrete) Prompt These prompts only contain tokens that directly correspond to words in the LLM vocabulary. Soft (continuous) Prompt These prompts contain tokens that may not correspond to any word in the vocabulary (Lester et al., 2021; Wang et al., 2023c). Soft prompts can be used when fine-tuning is desired, but modifying the weights of the full model is prohibitively expensive. Thus, a frozen model can be used while allowing gradients to flow through the prompt tokens. Hard Prompts ⊆ Soft Prompts A.2.4.3 Prediction Styles In LLMs, a prediction style is the format in which it predicts the next token. There are two common formats for this in prompting research. We do not discuss non-text prediction styles. Cloze In Cloze prompts, the token(s) to be predicted are presented as "slots to fill", usually somewhere in the middle of the prompt (Liu et al., 2023b). This is usually the case for earlier transformer models such as BERT (Chu and Lin, 2023). 61

62. Prefix In Prefix prompts, the token to be predicted is at the end of the prompt (Liu et al., 2023b). This is usually the case with modern GPT-style models (Radford et al., 2019b). 62

63. A.3 Datasheet We present a datasheet (Gebru et al., 2021) with more information about the associated paper dataset, which is hosted on HuggingFace. A.3.1 Motivation For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. This dataset was created to gather existing literature on prompt engineering in order to analyze all current hard prefix prompting techniques. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? This research was associated with the University of Maryland, Learn Prompting, and sponsored by OpenAI, but not created on the behalf of any particular organization. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. OpenAI contributed $10,000 in credits for their API. A.3.2 Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, coun- tries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. The dataset contains 1,565 research papers in PDF format. Any duplicate papers were removed automati- cally, though some could exist. What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description. Each data instance is a research paper as a PDF. Is there a label or target associated with each instance? If so, please provide a description. No Is any information missing from individual instances? If so, please provide a description, ex- plaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. No. Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. The papers were gathered in a semi-automated process which introduced the possibility of irrelevant papers being collected and relevant papers not being collected. There were manual reviews done for both possible errors to mitigate these errors. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? It is self-contained. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description. No. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. The dataset contains some papers on prompt injection. These papers may contain offensive content including racism and sexism. 63

64. A.3.3 Collection Process How was the data associated with each instance acquired? The dataset was compiled from Arxiv, Semantic Scholar, and ACL. What mechanisms or procedures were used to collect the data? We wrote scripts to automatically query the APIs of Arxiv and Semantic Scholar. Over what timeframe was the data collected? The dataset was curated the duration of the research paper, primarily in February of 2024. Were any ethical review processes conducted? No. A.3.4 Preprocessing/ Cleaning/ Labeling Was any preprocessing/cleaning/labeling of the data done? After collecting data from different sources, we removed duplicate papers and did a manual and semi- automated review of papers to ensure they were all relevant. Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? No, we do not anticipate the use of our preprocessed data. However, raw data can be recovered from the links we store. Is the software that was used to preprocess/clean/label the data available? It is contained within our code repository on Github. A.3.5 Uses Has the dataset been used for any tasks already? No. Is there a repository that links to any or all papers or systems that use the dataset? Yes. Is there anything about the composition of the dataset or the way it was collected and prepro- cessed/cleaned/labeled that might impact future uses? All of the papers we collected were written in English. It is possible some papers were not included due to a translation not being available. Are there tasks for which the dataset should not be used? No. A.3.6 Distribution Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? No. A.3.7 Maintenance Who will be supporting/hosting/maintaining the dataset? Our team will continue maintenance. How can the owner/curator/manager of the dataset be contacted? Please email us at sanderschulhoff@gmail.com Is there an erratum? No. If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? Yes, anyone is free to use/modify the data. 64

65. A.4 Keywords Here are the keywords we used for search. • jailbreak prompt • prompt an llm • prompt a large language model • prompt injection • prompt optimization • prompt engineering • few-shot learning • few shot learning • prompt-based methods • prompt based methods • prompting-based methods • prompting based methods • few-shot prompt • few shot prompt • one-shot prompt • one shot prompt • few-shot prompting • few shot prompting • one-shot prompting • one shot prompting • prompting techniques • prompt engineering techniques • llm prompting • large language model prompting • 0-shot prompt • 0 shot prompt • zero-shot prompt • many-shot prompt • zero-shot prompting • many-shot prompting 65

66. • in-context learning • in context learning • transformer model prompts • prompt-based transfer learning • nlp prompting strategies • llm interpretability via prompts • curriculum learning with prompts • feedback loops in llm prompting • human-in-the-loop prompting • token-efficient prompting • multimodal prompting • instruction prompting • prompt templating • prompt template 66

67. A.5 Evaluation Table ID M ODEL (Kocmi and Federmann, 2023b) (Lu et al., 2023c) (Fernandes et al., 2023) (Kocmi and Federmann, 2023a) (Araújo and Aguiar, 2023) (Wang et al., 2023b) (Liu et al., 2023d)† (Chan et al., 2024) (Luo et al., 2023) (Hada et al., 2024) (Fu et al., 2023a) (Gao et al., 2023c) (Chen et al., 2023g) (He et al., 2023a) (Sottana et al., 2023) (Chen et al., 2023c) (Zhao et al., 2023b) (Wu et al., 2023b) (Wang et al., 2023i) (Jia et al., 2023) (Yue et al., 2023) (Li et al., 2023e) (Liu et al., 2023f) (Bai et al., 2023b) (Dubois et al., 2023) (Liu et al., 2023h)† (Wang et al., 2023h) (Zeng et al., 2023) (Zheng et al., 2023b) (Lin and Chen, 2023) GPT-family Dav3, Turbo, GPT-4 PaLM GPT-4 ChatGPT ChatGPT GPT-3.5, GPT-4 ChatGPT, GPT-4 ChatGPT GPT4-32K GPT3, OPT, FLAN-T5, GPT2 ChatGPT ChatGPT GPT-4 GPT-4 GPT, Flan-T5 GPT-3.5, GPT-4 GPT-3 PaLM 2-IT-L LLaMa7b ChatGPT, Alpaca, Vicuna, GPT-4 GPT-3.5, GPT-4, Bard, Vicuna ChatGPT, Vicuna, chatGLM, StableLM GPT-4, Claude, ChatGPT, Bard, Vicuna GPT-4, ChatGPT, Dav3 GPT-4-32K Turbo, ChatGPT, GPT-4, Vicuna GPT-4, ChatGPT, LLaMA-2-Chat, PaLM2, Falcon Claude-v1, GPT-3.5, GPT-4 Claude-v1.3 P ROMPT O UTPUT S PACE Roles CoT Definition Few-Shot ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ DA, sMQM, stars, classes Error Span → Score Error Span Error Span Likert [1-5] DA, stars Likert [1-10] Likert [1-10] yes/no;A/B; Likert [1-10] [0,1,2] or binary Probability Likert [1-5], Pairwise, Pyramid, 0/1 Likert [1-10]; yes/no; pairwise: A/B/C Likert [1-5] Likert [1-5] Yes/No true/false pairwise voting A/B Probability Yes/No Pairwise continuous [0-1] Likert [1-5] pairwise Likert [1-5] Likert [1-10] Pairwise Pairwise/Likert [1-10] Likert [0-5], Likert [0-100] T YPE R ES . B ATCH E E I I E E I I E E I E E&I E E E E E E I E I E E E E E E E E S S S S S S M M S S S S S S S S S M M S S M S S M S M S S/M S ✓ ✓ ✓ ✓ ✓ ✓ ✓ Table A.2: Evaluation Paper Summary. E: Explicit (whether the model generates an assessment), I: Implicit (whether an assessment is derived from the model output); Response (Res.) S: Single response, M: Multiple responses; †: Model generated instruction; 67

68. A.6 Entrapment Prompting Process This section contains the thought process of our prompt engineer as he developed the prompt. A.6.1 Exploration • First did a bit of dataset exploration, looking at length/label distribution, then chose entrapment to start with. • Checked if gpt-1106-preview understood entrapment, by asking it to define the term WRT SCS. It did not. A.6.2 Getting a Label • Showed it the definition of entrapment in the system prompt and asked it to label a data point, but it responded saying I should seek mental health support. • I put the instructions in the user prompt, but get a similar response. • Appending "Is this entrapment? Yes or no" got it to actually respond with a label. • I gave it a reject example, but it labeled it as accept, and also sometimes gives emotional responses about getting help. • Using 10-shot prompting, it still gives the get help responses. • I tried removing the definition, got the same problem. It seems that it doesn’t like "accept"/"reject" as labels. • I added back the definition, changed labels to entrapment/not entrapment, it often says "not enough info to determine". • I switched to GPT-4-32K, which gave one word responses and correctly predicted a reject and an accept datapoint. A.6.3 Varying Prompting Techniques • Tested 0-shot with context. • 10-shot with context, showed improvement. • Taking first failing instance into playground. (12th in train file) • Tried doing ’A: Let’s go step by step and figure this out: ", output "A: Yes" • Did "A: Let’s go step by step and figure this out: ", OUTPUT: The individual in this post is expressing feelings of hopelessness, loneliness, and despair. They mention a significant loss (their mother) and a subsequent decline in their mental health, turning to alcohol as a coping mechanism. They also mention financial struggles and the possibility of becoming homeless. They express feelings of guilt and shame about their situation and compare themselves negatively to their friends and family. Most concerning is their detailed plan for suicide, involving sleeping pills and alcohol. They also mention considering stepping in front of a car. Given these factors, the label for this post would be "Yes" for expressing feelings of entrapment. 68

69. • "A: Before predicting a label, let’s go step by step and write out your thought process. Use the items in the definition of entrapment to support your conclusion.", OUTPUT: Before predicting a label, let’s go step by step and write out your thought process. Use the items in the definition of entrapment to support your conclusion. • Began discussing with the AI "they don’t ever say they feel trapped or that there is no way out, how did u get that idea?", OUTPUT: The individual doesn’t explicitly state that they feel trapped or that there is no way out, but their narrative suggests a sense of entrapment. They mention that they are facing eviction, struggling with debt, and dealing with a decrease in income. They also express a sense of hopelessness and despair, and contemplate suicide as a potential solution to their problems. These elements suggest a feeling of being trapped in their current circumstances with no apparent way out. However, it’s important to note that this interpretation is based on the information provided in the post and the individual’s expressed feelings and thoughts. • Asked it to help reformulate instructions to make explicitness more clearly needed, still predicts entrapment. • Added "ENTRAPMENT MUST BE EXPLICIT, NOT IMPLICIT." to instruction at top after defini- tions (no CoT inducer) • Also tried with previous CoT inducer, still says entrapment in a hard to parse out way. • Added "If the person does not explicitly say they feel trapped, this doesn’t count as entrapment." (no CoT), still entrapment. • Talking to AI, it thinks "Today I found out I have 10 days to vacate my apartment or I’ll be formally evicted. I’m 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I’ll probably be homeless" is feeling trapped/stuck. • Tried "Before stating a label, write out reasoning using the grid above about why this may/may not be entrapment:", still thinks entrapment. • paste in email: [REDACTED] above definition of entrapment • remove shots, says "Entrapment" • add this after def: IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped., says "Yes" • In the prompt, gave it CoT reasoning. (18.txt), and tried with the next wrongly labeled one (15), (full prompt, 19.txt) • Tested this on everything except first 20, did pretty well • Tried removing email, performance dropped of a cliff • At this point, I am thinking that giving examples with reasoning helps (obviously) • Tried to add 10 shots in for free, before the last one with reasoning, bad results 69

70. A.6.3.1 AutoCoT • Develop dataset using this prompt (22.txt). Then ask it "Why?". If it disagrees, I say "It is actually not entrapment, please explain why." (accidentally duplicated email 23.txt) • Just for fun, tried 0 shot full context (had to adjust verbalizer) • tried this with special verbalizer which catches "This post does not meet the criteria for Entrapment." • Tested my generated data, beat 0.5 F1 • Doing 10 more exemplars w autocot. Sometimes responds immediately with reasoning like "This post does not meet the criteria for Entrapment as the individual does not explicitly express feelings of being trapped or hopeless.", so just use that if so. Sometimes get refusal "I’m really sorry to hear that you’re feeling this way, but I’m unable to provide the help that you need. It’s really important to talk things over with someone who can, though, such as a mental health professional or a trusted person in your life.", just ask "Explain why it is not entrapment." after if so. • performance didnt really improve, realized about 11% are getting -1, meaning not extracted properly. Retrying with full words "Question" instead of Q, also for reasoning and answer. • this led to higher inability to parse, at about 16%. A.6.3.2 Developing Answer Extraction • put first failing to parse one in (22), and developed a prompt for it. • did worse: (0.42857142857142855, 0.2857142857142857) 0.5051546391752577, 0.8571428571428571, • only using extracted label if have -1 helps slightly to (0.48, 0.61, 0.8571428571428571, 0.3333333333333333) • going back to best performing prompt–10 QRA shot, and performing extraction with any -1s, doesnt help other than gently boosting accuracy, perhaps when it doesnt answer A.6.3.3 Iterating on Email • tried best perf, with no email • tried with deduped email, worse results • noticed that ones its unsure about often contained 1 labels that should be 0, so trying to "recover" these doesnt help • try moving around exemplar order, performing extraction, didnt help • triplicated email, didnt help 70

71. A.7 Formally Defining a Prompt "Prompt" is a widely used term, but uses and definitions differ widely across research. As a result, it is difficult to create a formal, mathematical definition for a prompt. In this section, we outline some formalisms for prompt engineering. As a conditioning Mechanism. Qiao et al. (2022) present the following definition, which involves the prompt T and a question Q as conditioning mechanisms on predicting the next token. Note that they appear to use Brown et al. (2020)’s original definition of prompt, which refers to the non-question part of the prompt (e.g. few-shot exemplars, instructions). p(A | T , Q) = |A| Y p LM (a i | T , Q, a 1:i−1 ) (A.1) i=1 Here, the prompt and question condition the pre-trained LLM p LM . The a 1:i−1 are previously generated answer tokens and A a complete answer. Templating. The above formalization does not include the notion of maximizing a scoring or utility function (e.g. accuracy on a dataset), which prompts are often designed to do. Additionally, prompt engineers often seek to design prompt template rather than prompts. Here, we reformulate eq. (A.1) to include the prompt template: p(A | T (x ∗ )) = |A| Y p LM (a i | T (x ∗ ), a 1:i−1 ) (A.2) i=1 We replace Q with x ∗ ∈ D eval , an item from a dataset (e.g., evaluation data). Additionally, we replace Q on the right side with T (x). T (·) is a prompt template: a function that accepts some item as input then returns a prompt that is used to condition the model. Few-Shot Prompting. Often, an important part of the prompting process is the use of few-shot exemplars. D train is training data (used to build the prompt) and X is a test set for evaluation. D train = {(x 1 , y 1 ), (x 2 , y 2 ), ..., (x n , y n )} X = {x ∗ 1 , x ∗ 2 , ..., x ∗ m } (A.3) (A.4) In the few-shot setting, the prompt template function T (·) also takes as input one or more training samples X = {(x i , y i )} n 1 ⊂ D train ∗
p A | T (X , x ) = |A| Y p LM (a i | T (X , x ∗ ) , a 1:i−1 ) (A.5) i=1 Optimization. As mentioned, it is often desirable to speak about improving prompts (prompt templates, that is) with respect to a scoring function, usually defined with respect to a dataset. T ∗ = argmax E x i ,y i ∼D [S (p LM (A|T (x i )), y i )] (A.6) T In this definition, we are evaluating over a dataset D with respect to the scoring function S(·). S(·) evaluates the output A, generated by the LLM conditioned on the prompt T (§ ⟩ ). y i are labeled outputs that can be used by S. In some cases, there may not be any labeled data y i , and S(·) may be reference-free. 71

72. Other considerations. These formalisms could be adapted to cater to CoT, retrieval systems, and more. Here we describe a simple setup which is most descriptive of the prompting process without adding too much complexity. We also draw attention to the lesser known concept of answer engineering. E(A) is a transformation function over the raw LLM output that allows it to be compared to the ground truth. A ∼ p LM (A | T (x i ), y i ) ∗ T = argmax E x i ,y i ∼D [S (E(A), y i )] T ,E 72 (A.7) (A.8)

73. A.8 In-Context Learning Definitions Disambiguation Brown et al. (2020) seemingly offer two different definitions for ICL. All bolding in this section is our own. Recent work [RWC+19] attempts to do this via what we call “in-context learning”, using the text input of a pretrained language model as a form of task specification: the model is conditioned on a natural language instruction and/or a few demonstrations of the task and is then expected to complete further instances of the task simply by predicting what comes next. However, they later appear to define it as few-shot only: For each task, we evaluate GPT-3 under 3 conditions: (a) “few-shot learning”, or in-context learning where we allow as many demonstrations as will fit into the model’s context window (typically 10 to 100), (b) “one-shot learning”, where we allow only one demonstration, and (c) “zero-shot” learning, where no demonstrations are allowed and only an instruction in natural language is given to the model. However, they include this image that clarifies the matter: Figure A.1: ICL from Brown et al. (2020). Additionally, they explicitly state that ICL does not necessarily involve learning new tasks. 73

74. To avoid this confusion, we use the term “meta-learning” to capture the inner-loop / outer-loop structure of the general method, and the term “in context-learning” to refer to the inner loop of meta-learning. We further specialize the description to “zero-shot”, “one-shot”, or “few- shot” depending on how many demonstrations are provided at inference time. These terms are intended to remain agnostic on the question of whether the model learns new tasks from scratch at inference time or simply recognizes patterns seen during training – this is an important issue which we discuss later in the paper, but “meta-learning” is intended to encompass both possibilities, and simply describes the inner-outer loop structure. We use Brown et al. (2020)’s broad definition, though note that practitioners often use ICL to refer to situations in which the model appears to be learning new tasks from the prompt. Our definition differs from Dong et al. (2023)’s formal definition, even though it is also derived from (Brown et al., 2020). 74

75. A.9 Contributions The following are the contributions made by the team members in various sections of this paper. Most authors conducted reviews of other sections as well. Advisors • Denis Peskoff: Assisted with paper organization and final review. • Alexander Hoyle: Provided guidance on writing, meta-analysis approach, and ran automated baselines for case study. • Shyamal Anadkat: Assisted with the overall review of the paper and the etymology and definitions. • Jules White: Built trees for technique taxonomies. • Marine Carpaut: Framed, reviewed and suggested papers for the multilingual section. • Phillip Resnik: Principal Investigator SCS Labeling • Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker: reviewed and gave advice for this section. Benchmarking and Agents • Konstantine Kahadze: Team leader for the Benchmarking section; managed MMLU benchmarking codebase, contributed to Security and Meta Analysis. • Ashay Srivastava: Team leader for the Agents section, reviewed papers for human review, worked on the tool use agents section. Worked on the compilation of contributions. • Hevander Da Costa: Contributed to the Benchmarking section and Meta Review datasets list, reviewed literature on LLM code generation and prompting techniques. Added literature review content to the Agents section. • Feileen Li: Worked on the tool use agents section, assisted with the human paper review. Alignment and Security • Nishant Balepur: Team leader for the alignment section, helped with high-level discussions in benchmarking, and reviewed drafts. • Sevien Schulhoff: Team leader for the security section and contributed to the benchmarking section. Related Works and Section Contributions • Chenglei Si: Suggested related works and edited section 2.2 and section 7. • Pranav Sandeep Dulepet: Contributed definitions for section 2 and worked on segmentation and object detection in the multimodal section. • HyoJung Han: Contributed to the Multimodal section, especially the speech+text part, and wrote the audio prompting section. • Hudson Tao: Authored sections on image, video, and 3D within multimodal, reviewed papers for human review; maintained GitHub codebase, and built the project website. • Amanda Liu: Authored taxonomic ontology sections, conducted background research for introduc- tion and related work, developed code pipelines for meta-analysis graphs 75

76. • Sweta Agrawal: Team lead for evaluation section. • Saurav Vidyadhara: Assisted with general review and revising taxonomy trees. • Chau Pham: Assisted with meta review, including automated analysis of topics. Multilingual Prompting and Meta Analysis • Dayeon Ki: Led the Multilingual prompting section, conducted review on related papers, and wrote Section 3.1. • Yinheng Li: Worked on section 2.2 text-based techniques, reviewed techniques, and contributed to drafting figure 2.2. • Saloni Gupta: Wrote tests for paper compilation, helped set up paper pipeline, and worked on the code diagram and grammar for the paper. • Gerson Kroiz: Involved with section 1.1 and defining a prompt. • Aayush Gupta: Contributed to the Meta Analysis, compiling papers, and generating visualization graphs. • Michael Ilie: Co-Lead Author, managed codebase, ran experiments, collected data, and helped with various sections including the PRISMA review figure and the SCS prompting case study. • Sander Schulhoff: Lead Author 76