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acl-experiments

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Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.

ACL-Skills/skills/acl-experiments/SKILL.md

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# 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)
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