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This research addresses the inefficiency of current LLM-as-a-judge leaderboards by introducing a latent variable framework that explicitly corrects for systematic biases like position and verbosity effects. By modeling these confounders, the method achieves statistically rigorous rankings with significantly fewer comparisons, offering a more sustainable path to trustworthy AI evaluation without incurring excessive computational costs.
Accounting for Bias Enables Sustainable LLM Evaluation
arXiv:2609.31184v1 Announce Type: cross Abstract: LLM-as-a-judge has become the de facto standard for scalable, subjective evaluation, yet current leaderboards compensate for systematic measurement bias by running ever more comparisons, an approach that is both statistically unsound and computationally wasteful. The root cause is an incomplete measurement model, treating LLM judges as neutral, interchangeable instruments ignores documented biases like position bias, verbosity bias, judge severity, and self-enhancement, that no volume of additional data can eliminate. We propose a unified latent variable framework that jointly models pairwise and ordinal data while explicitly correcting for these confounders, recovering reliable rankings from substantially fewer comparisons. Because fitting this model costs negligible compute relative to a single round of LLM inference, bias correction is not only more statistically rigorous but also a more sustainable approach to trustworthy evaluation.
Abstract:LLM-as-a-judge has become the de facto standard for scalable, subjective evaluation, yet current leaderboards compensate for systematic measurement bias by running ever more comparisons, an approach that is both statistically unsound and computationally wasteful. The root cause is an incomplete measurement model, treating LLM judges as neutral, interchangeable instruments ignores documented biases like position bias, verbosity bias, judge severity, and self-enhancement, that no volume of additional data can eliminate. We propose a unified latent variable framework that jointly models pairwise and ordinal data while explicitly correcting for these confounders, recovering reliable rankings from substantially fewer comparisons. Because fitting this model costs negligible compute relative to a single round of LLM inference, bias correction is not only more statistically rigorous but also a more sustainable approach to trustworthy evaluation.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
arXiv:2609.31184 [cs.AI]
(or arXiv:2609.31184v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.31184 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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