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arXiv Machine Learning By Fariya Afrin, Ibne Farabi Shihab

When Accuracy Gaps Fail to Certify: Auditing Cross-Domain Recalibration of LLM Judges

Read the original on arXiv Machine Learning →

The study examines whether accuracy gaps between source and target tasks can certify the failure of scalar recalibration maps for large language model judges. Across thirteen judges, two generators, eight domains, and 1,176 transfers, the accuracy gap only provides a weak lower bound on target calibration error and can predict opposite outcomes. Even with a finite‑sample lower certificate, the method shows low power (0.13) and does not reliably indicate when recalibration will fail.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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