OSCAR: Order-aware Sensitivity Correction for AI Rankings
LLM judges evaluate candidate language models through pairwise comparisons of their responses. Judge-aware Bradley--Terry models aggregate pairwise comparisons by jointly estimating candidate scores and judge-specific sensitivities (how strongly each judge responds to quality differences), often interpreted as reliability. We show that this reading fails when judges prefer a display position. Even under balanced display orders or swap-and-average, omitting position attenuates fitted judge sensitivity by approximately the factor $1-D^2$ near equal quality, where $D$ is the judge's net first-slot preference between equally good responses. We propose OSCAR, which jointly estimates candidate ...
| Authors | You Liu, Yue Liu, Quanchao Lu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 21 September 2026 |
| Last updated at source | 13 October 2026 |
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