colm-artifact-evaluation
GitHub指导COLM论文作者打包模型权重、数据等实验产物，应对匿名评审与公开发布。涵盖分类清单、一键复现脚本构建及去隐私化处理，重点解决版权许可和API条款合规问题。
触发场景
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-artifact-evaluation -g -y
SKILL.md
Frontmatter
{
"name": "colm-artifact-evaluation",
"description": "Use when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public post-acceptance release, navigating licenses, API terms-of-service limits, and the absence of a formal COLM artifact track."
}
COLM Artifact Evaluation
COLM had no formal artifact-evaluation track verifiable for the 2026 cycle (checked 2026-07-08; 待核实 each edition). That absence does not lower the bar — it moves the audit into ordinary review, where artifact quality influences scores without a rubric to appeal to. Package as if a skeptical reviewer will spend ten minutes with your materials, because at this venue one usually will.
The COLM artifact taxonomy
LM papers produce artifact types with very different release mechanics; inventory yours before deciding anything:
| Artifact | Review-time form | Release-time form | Blocking question |
|---|---|---|---|
| Code (training/eval) | Anonymized repo or supplement ZIP | Public repo, tagged release | Does one command reproduce one table? |
| Prompts | Verbatim appendix + files in package | Same, public | Exact strings, incl. system prompts? |
| Fine-tuned weights | Usually described, not uploaded (size) | Model hub upload with model card | Does the base model's license permit derivative release? |
| Training/eval data you built | Anonymized sample + datasheet | Full release with license | Any personal data, scraped ToS conflicts, or annotator-privacy issues? |
| Cached model outputs | Sample in supplement | Full archive | Does the provider's ToS permit publishing outputs at this scale? |
| Human-eval materials | Instructions + interface in appendix | Same | IRB/consent status stated? |
The two questions authors most often skip are in the right-hand column: derivative weight licensing and output-publication ToS. Both can void a promised release after acceptance — resolve them before the paper commits to anything.
One-command reproduction
The credibility core of the package is a single entry point per headline result:
# Makefile at package root — one target per main-text table/figure
table2: ## headline comparison, ~40 GPU-min or ~$8 API spend
python run_eval.py --config eval/run-042.yaml --out results/table2.csv
figure3: ## scaling curve from cached outputs (no model access needed)
python plots/scaling.py --cache outputs/cache.jsonl --out figs/figure3.pdf
verify: ## regenerate all numbers from cached outputs only
python verify_from_cache.py --tolerance 0.1
The verify-from-cache target is the LM-specific trick: reviewers without GPUs or
API budgets can still confirm that your published numbers follow from your recorded
model responses. It converts "trust me" into "check me" at zero compute cost, and it
keeps working after API models drift or deprecate (colm-reproducibility).
Anonymous review packaging
- Build from a clean export and grep for identity leaks — the commands and channel
list live in
colm-supplementary; org-scoped model-hub IDs are the leak class unique to LM work. - If weights must be inspectable at review time, an anonymized hub account or a size-reduced distilled checkpoint are the workable options; a link to your lab's account is a double-blind violation under COLM's no-identifying-links rule.
- Include a
MANIFEST.md: inventory, license per item, compute needed per target, and what is deliberately absent with the reason ("training corpus omitted: contains licensed text; filtering scripts included instead").
Datasheets for anything you release
For each dataset or evaluation set: how items were created (author population, LLM-generated fraction — disclosable under the 2026 LLM policy), collection dates (this doubles as contamination documentation for future users), license and source licenses, known biases and coverage gaps, and a contamination canary if you want future training runs to be detectable. For model releases: intended use, evaluation scope, and known failure modes. These documents are cheap at packaging time and impossible to reconstruct honestly later.
Post-acceptance release sequence
- De-anonymize the repository; restore real hub org names and W&B links.
- Upload weights/data with cards and licenses; tag the exact code release cited in the camera-ready.
- Add the paper's OpenReview URL to every artifact so provenance points both ways.
- Freeze a
colm2026git tag — the paper cites a snapshot, not a movingmain. - Update the paper's availability statement to match what actually shipped
(
colm-camera-readyowns the deadline: August 7, 2026 this cycle).
The ten-minute reviewer walkthrough
Dry-run the package as the busiest plausible reviewer before every upload. The sequence they follow is predictable, so optimize for it in order:
- Minute 1-2: open
MANIFEST.md. If there is no manifest, they grep for a README and form their opinion from whatever half-stale file they find. The manifest is the cheapest score you will ever buy. - Minute 3-4: look for the entry point.
make table2or an equivalent single command. If the first thing they see is a 400-line setup guide with cluster assumptions, the walkthrough ends here. - Minute 5-7: run
verifyfrom cache. No GPUs, no keys, under a minute of compute — this is the step that actually gets executed in practice, which is why the cached-outputs archive earns its place in the package. - Minute 8-9: spot-check a prompt file against the paper's appendix. Any mismatch between packaged prompts and printed prompts contaminates trust in both.
- Minute 10: skim the code for identity leaks and hardcoded secrets — partly
ethics-duty, partly curiosity. This is where an org-scoped
from_pretrainedstring ends your anonymity.
Sizing note: keep the review package lean — cached outputs can be sampled down to
what verify needs, with the full archive promised for release. No supplementary
size cap was verifiable for COLM 2026 (待核实), but a multi-gigabyte upload fails
socially even where it succeeds technically.
Longevity: the two-year test
A COLM artifact's real audience arrives later — the group in two years trying to
compare against you. Two cheap investments serve them: freeze an environment
manifest (exact package versions; a container digest if you can), and write the
MANIFEST.md assuming every external URL in it will eventually rot — name
artifacts by content hash where possible so mirrors stay verifiable. The venue
publishes on OpenReview, where your artifact links are permanently attached to the
paper's public page; links that die quietly are the failure mode, so prefer
archival hosts for anything you would want cited.
Output format
[Inventory] code ▢ prompts ▢ weights ▢ data ▢ cached-outputs ▢ human-eval ▢
[Legal gates] base-model license: <ok/blocks release> output-ToS: <ok/limits>
[One-command check] targets exist for: <tables/figures> verify-from-cache: ▢
[Anonymity] package clean / leaks: <items>
[Manifest + datasheets] present / missing: <which>
[Release plan] <ordered steps with dates>
版本历史
- 9f86f09 当前 2026-07-19 14:40
