ACL Artifact Evaluation
Use this to plan the evidence package around an ACL paper. ACL has no separate
artifact-badge track; instead, artifact scrutiny is folded into review through
the supplement archive and Section B ("scientific artifacts") of the
Responsible NLP checklist, which reviewers cross-check against the PDF.
What counts as an artifact here
- Code: training/inference scripts, evaluation harnesses, prompt templates.
- Data: new corpora, annotations, filtered subsets of existing corpora, test
suites, adversarial sets.
- Model outputs: generations, ranked lists, logits used in analysis — often the
cheapest way to make an LLM paper checkable without GPUs.
- Human-subject materials: annotation guidelines, interface screenshots,
consent text, compensation description.
Submission-time packaging rules
- Supplements upload as .tgz/.zip through the OpenReview form; links to
tracked cloud storage are not acceptable, and any linked page must be
anonymous.
- Scrub identity everywhere reviewers can look: file paths, git metadata,
notebook author fields, license headers, dataset hosting pages, README
contact lines.
- Reviewers are not required to open supplements. The paper plus checklist
must stand alone; the archive is for verification, not for essential content.
Checklist items your artifact must satisfy
| Responsible NLP item (Section B) |
Artifact implication |
| Cited creators + versions of used artifacts |
Pin dataset/model versions in the README and bibliography |
| License / terms of use stated |
Include the license you release under and those you consumed under |
| Use consistent with intended use |
Justify research use of scraped or user-generated data |
| PII and offensive content handled |
Describe scanning/anonymization steps actually performed |
| Documentation of domains, languages, demographics |
Ship a data statement or datasheet, not just row counts |
| Statistics on splits reported |
Train/dev/test sizes in both paper and README |
Checklist answers contradicted by the archive read as misleading information —
grounds for desk rejection under ARR policy, and a credibility wound even when
not enforced.
What an ACL reviewer opens first
- The README — it has roughly one minute to orient them.
- Prompt files and evaluation scripts, for any LLM claim: exact prompts,
decoding parameters, and scoring code are the reproduction spine.
- Annotation guidelines, for any dataset or human-eval claim: reviewers judge
whether the labels could possibly mean what the paper says.
- A sample of the data itself — quality problems visible in twenty random
examples have sunk otherwise strong resource papers.
Vignette: packaging a multilingual benchmark submission
A hypothetical paper releases a 7-language reading-comprehension test suite
built from news text plus a baseline evaluation of five LLMs.
- Ship per-language provenance: source, license, collection window, and the
filtering pipeline as runnable code, since "web text" alone fails checklist
item B on documentation.
- Include annotator guidelines, pay, recruitment channel, and agreement
statistics; multilingual annotation quality is the first attack surface.
- Provide the exact prompts and outputs for all five models so reviewers can
re-score without API keys.
- Keep a versioned, hash-stamped test file so post-publication contamination
can be audited later.
Release ladder after acceptance
anonymous supplement -> public repo + dataset page -> archived, versioned release
(review-time) (camera-ready links) (DOI/hub artifact, cited version)
Post-acceptance, register the artifact where your community actually looks
(model/dataset hubs, a maintained repo), state the license explicitly, and put
the citation-of-record (the Anthology entry) in the README.
Anonymization sweep, concretely
Run these before zipping, on a copy:
# authorship trails in code and docs
grep -ri "yourname\|yourlab\|university" . --include="*.py" --include="*.md"
# git history and remotes leak owners
rm -rf .git; # or re-init a fresh repo for the archive copy
# notebook metadata carries usernames and kernel paths
jupyter nbconvert --clear-output --inplace *.ipynb
# absolute paths in configs and logs
grep -r "/home/\|/Users/" . | head
Then check the parts tools miss: license headers naming the lab, dataset
hosting pages with institutional branding, model cards listing maintainers,
and README badges pointing at owner-named CI.
Sizing and format sanity
- Keep the archive lean: strip checkpoints reviewers cannot load anyway,
cached datasets, and virtualenvs; describe big assets and provide them at
camera-ready instead.
- One top-level README, one environment file, one entry point per claimed
result — reviewers grant roughly a minute before giving up.
- Verify the .zip/.tgz opens on a machine that has never seen the project;
OpenReview upload limits and accepted fields vary by cycle, so check the
live form rather than last cycle's.
Output format
[Artifact role] anonymous supplement / camera-ready release / public benchmark
[Contents] <code/data/prompts/outputs/guidelines>
[Checklist alignment] <Section B items satisfied vs missing>
[Anonymity findings] <paths/metadata/hosting leaks>
[Release plan] <post-acceptance registry, license, versioning>
