ACL Experiments

Use this while the experimental story can still change. The ACL evidence bar is not "beats the baseline once": it is a defensible measurement of a language capability, with the failure modes examined.

Baseline honesty

• Include the strongest cheap baseline: a well-prompted current LLM has become

mandatory context for most tasks — a method beating only pre-LLM systems invites the "does this matter now?" review.

• Tune baselines with the same care as your method (same search budget, same

data); reviewers explicitly probe for asymmetric tuning.

• Report the trivial baselines (majority class, copy input, retrieval-only)

when they contextualize how hard the task actually is.

Evaluation design

• Breadth must match the claim: a "general" claim needs multiple datasets;

a cross-lingual claim needs typologically distinct languages, not three Romance neighbors.

• Automatic metrics need justification for generation tasks — pair n-gram or

embedding metrics with human or LLM-judge evaluation, and validate any LLM-judge against human labels before leaning on it.

• Fix the evaluation protocol before final runs: dev-set peeking on the test

set via repeated submissions is unreportable and unrepairable.

Statistical floor

| Result flavor | Required rigor at ACL | |---|---| | Small deltas between systems | Significance test (bootstrap/permutation) or overlapping-interval honesty | | Fine-tuning results | Multiple seeds; mean and deviation in the table, defined in the caption | | Prompted-LLM results | Multiple prompt paraphrases and/or samples; sensitivity range reported | | Human evaluation | Raters per item, agreement statistic (e.g., Krippendorff's alpha), pay disclosed | | Correlation claims (metrics) | Confidence intervals and comparison against existing metric correlations |

The Responsible NLP checklist (Section C) asks for descriptive statistics and error bars — an experiment plan that cannot fill Section C truthfully is incomplete by construction.

Contamination and validity controls

• Reason explicitly about test-set membership in pretraining data: release

dates vs model cutoffs, overlap scans, or held-back fresh test items.

• Watch prompt leakage: few-shot exemplars drawn from the test distribution,

instructions embedding label hints.

• For annotation-based data, quantify label quality before measuring models

against it; models are now frequently better than noisy gold labels.

Ablations and the mechanism claim

• Each component the abstract credits needs an ablation row; each ablation

row needs the same variance treatment as the headline number.

• Prefer ablations that test the explanation (e.g., "gains come from the

retrieval step") over combinatorial component sweeps.

• Scale ablation: if a claim is "method X helps," show it at two model sizes

or state the single-scale limitation explicitly.

Error analysis as a deliverable

The distinctive ACL expectation: a quantitative error analysis with named categories.

1. Sample failures (100-200) from the strongest configuration.
2. Induce 4-8 functional error categories; double-annotate a subset and

report agreement.

1. Report category frequencies for your method vs the best baseline —

where do gains actually come from?

1. Feed the two most persistent categories into Limitations.

Pre-run design worksheet

text
Claim:         <one sentence>
Datasets:      <n, why these, language list>
Baselines:     <incl. tuned LLM baseline + trivial floor>
Runs/variance: <seeds or prompt paraphrases; interval type>
Significance:  <test, when applied>
Human eval:    <items, raters, agreement plan, pay>
Contamination: <audit method>
Ablations:     <component -> table row>
Error analysis:<sample size, category plan>

Common evidence failures seen in ARR reviews

• Averaging over languages to hide that one language regressed — report the

per-language block; reviewers open the appendix table first when a claim says "multilingual."

• Comparing your tuned method against baseline numbers copied from papers

that used different preprocessing or splits.

• Treating an LLM judge as ground truth without reporting its agreement

with humans on a calibration subset.

• Claiming efficiency without wall-clock, memory, or cost on matched

hardware.

• Running the significance test only on the comparison that wins.
• Reporting the best seed as the headline and the mean in the appendix —

reviewers call this out by name.

When compute is the constraint

• Pre-register (internally) which single configuration gets the full

multi-seed treatment, and make it the headline setting.

• Use paired designs — same items, both systems — so smaller samples still

yield tight comparisons and permutation tests apply cleanly.

• Prefer breadth at small scale plus depth at one large scale over a thin

sweep of everything; state the choice in the setup section.

• Cache and release intermediate outputs so ablations re-score rather than

re-run.

Output format

text
[Evidence verdict] convincing / thin / misaligned-with-claim
[Baseline gaps] <missing or under-tuned comparators>
[Statistical gaps] <variance/significance/agreement omissions>
[Validity threats] <contamination/leakage/label-quality>
[Highest-value next run] <one experiment>