feat: judge reliability - calibration, uncertainty, and bias debiasing (v0.3.0)#2
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Extract an un-cached single-shot score() from JudgeDimension.evaluate and refactor evaluate to reuse it (cache behavior unchanged). Add a SelfConsistencyJudge that samples a base judge N times via score() and reports the score distribution (mean, pstdev, normal-approx 95% CI). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Implements PairwiseJudge that runs A-vs-B comparisons in both orders and only trusts a verdict consistent across the swap, falling back to a tie otherwise. Adds parse_pairwise, position_bias_rate diagnostic, default prompt template, and an offline test suite. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Append pearson/spearman/mae to bench.metrics and add judge.calibration (CalibrationSample, CalibrationReport, calibrate_judge, verbosity_bias) measuring score-level and label-level agreement plus verbosity bias. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Top-level + judge/ + bench/ __init__ exports for SelfConsistencyJudge, ScoreDistribution, PairwiseJudge, PairwiseResult, position_bias_rate, calibrate_judge, CalibrationSample, CalibrationReport, verbosity_bias, and the pearson/spearman/mae stats. - examples/judge_calibration.py: offline demo of uncertainty, position-bias debiasing, calibration, and verbosity-bias. - README: "Judge reliability" section. - version 0.2.0 -> 0.3.0.
…sed) Reviewer follow-up (MINOR). Other reviewer suggestions deferred to v0.3.1: verbatim raw in PairwiseResult, and the evaluate/score double-render.
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Summary
An LLM judge is itself a model, with variance and bias. This release adds the tools to measure and correct for that, so a judge can be defended in a design review instead of trusted on faith. All three pieces use the existing
Dimensioninterface and stay offline-testable via injected callables.Uncertainty: self-consistency (
SelfConsistencyJudge)Samples a judge N times and reports the mean, spread, and a 95% confidence interval, so you know whether a score is solid or a coin flip.
JudgeDimension.score()was added as an un-cached single-shot scorer (the existingevaluatewas refactored to delegate to it; cache and graceful-failure behavior unchanged).Position-bias debiasing:
PairwiseJudgeRuns a pairwise comparison in both answer orders and only trusts a verdict that survives the swap; if the judge flips, it returns a tie with
consistent=False.position_bias_rate()quantifies how often a judge flips across a set of pairs.Calibration:
calibrate_judgeMeasures judge-vs-human agreement: Pearson/Spearman correlation and MAE against human scores, plus accuracy and Cohen's kappa against human pass/fail.
verbosity_bias()checks the failure mode where a judge just rewards length. Newpearson/spearman/maestats added tobench.metrics.Quality
examples/judge_calibration.pydemos all three tools).Deferred to v0.3.1 (reviewer follow-ups, non-blocking)
PairwiseResult.raw(currently reconstructed).JudgeDimension.evaluate.Packaging