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Guidelines for Rigorous NLP Experimental Design

acl-experiments

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for researchers designing and auditing NLP experiments for top-tier conferences. It covers establishing defensible baselines, ensuring evaluation breadth, performing rigorous statistical tests (significance, variance), controlling for data contamination, and structuring detailed functional error analysis. Essential reading for publishing reliable AI research.

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