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.
How to install acl-experiments?
brycewang-stanford/awesome-journal-skills/acl-experiments
$npx -y skills add brycewang-stanford/awesome-journal-skills --skill acl-experimentsInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/brycewang-stanford/awesome-journal-skills" --skill "brycewang-stanford/awesome-journal-skills/acl-experiments"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/brycewang-stanford/awesome-journal-skills" that are relevant to the current task. Run `npx skills add "https://github.com/brycewang-stanford/awesome-journal-skills"` and select the relevant skills, then follow their instructions.
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