From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation
Evaluating new large language models typically requires costly human annotation campaigns at scale. LLM-as-a-judge offers a cheaper alternative, but judge scores carry systematic errors - such as position bias, self-preference, or intransitivity - that can strongly miscalibrate the resulting rankings. We quantify the resulting judge-human disagreement at two complementary levels. At the local level, we estimate per-battle uncertainty from the judge's own score differences by propagating calibrated win probabilities rather than hard labels into the Bradley-Terry procedure. This alone provides a drastic improvement to Elo estimation accuracy, bringing LLM-derived ratings within 17.9 Elo MAE...
| Authors | Bora Kargi, David Salinas |
|---|---|
| Primary category | cs.LG |
| Published | 11 June 2026 |
| Last updated at source | 12 June 2026 |
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