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  "Title": "Statistical Inference for Language Model Evaluations",
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  "Authors@R": "person(\"Charles\", \"Coverdale\", email = \"charlesfcoverdale@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "Treats language model evaluations as statistical\nexperiments and supplies the inference they require. Provides\ncentral limit theorem and cluster-robust standard errors for\nevaluation scores, paired and unpaired model comparisons,\nvariance decomposition when several responses are drawn per\nquestion, control-variate variance reduction, multiplicity\nadjustment across benchmark suites, and power and minimum\ndetectable effect calculations for planning evaluations,\nfollowing Miller (2024) <doi:10.48550/arXiv.2411.00640>. For\nevaluations scored by a model judge, implements agreement\nstatistics against a human gold standard and prediction-powered\ninference (Angelopoulos et al. 2023)\n<doi:10.1126/science.adi6000> with the power-tuned estimator of\nAngelopoulos, Bates and Jordan (2023)\n<doi:10.48550/arXiv.2311.01453>, so a small set of human labels\ndebiases a large set of judge scores. Leaderboards are\nsupported through bootstrap rank intervals and Bradley-Terry\nratings (Bradley and Terry 1952) <doi:10.2307/2334029>. Accepts\nscores from any evaluation harness.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-GB",
  "URL": "https://charlescoverdale.github.io/evaluatellm/,\nhttps://github.com/charlescoverdale/evaluatellm",
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