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eacl-experiments
Use when designing or auditing the empirical evidence for an EACL paper, covering tuned and LLM baselines, multilingual breadth matched to the claim, significance and variance floors, human-evaluation agreement, data-contamination controls, ablations, and error taxonomies, so that every stated result is measured rather than asserted.
EACL-Skills/skills/eacl-experiments/SKILL.md
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# EACL Experiments
Use this to make an EACL paper's evidence hold up under NLP review. EACL rewards **well-scoped
questions answered with careful controls** over leaderboard maximalism — its best papers include
analyses and critiques, not only state-of-the-art systems (see
`../../resources/exemplars/library.md`). Design the evidence to match the claim exactly, no
broader.
## Baselines that make a comparison fair
- Include a **tuned** baseline, not a strawman: an under-tuned competitor makes a win
meaningless. State the search space for both your method and the baselines.
- For LLM-based work, include the **obvious prompt/few-shot baseline** and report its prompts and
decoding settings; a gain over an unreported baseline is not credible.