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arXiv Computation and Language By Xinyu Li, Yi Zhou, Guanqun Cao, Zeyu Fu, Tianjin Huang, Gaojie Jin

Localize-Then-Decide Guarantees for LLM Judgments

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Large language models (LLMs) are increasingly used to evaluate output quality, but guaranteeing agreement with human judgments is difficult. The paper introduces a Localize-Then-Decide framework that first uses conformal prediction to narrow down a shortlist likely to contain the human-preferred response, then applies a calibrated confidence rule to select a single response or abstain. Experiments show this two-stage approach consistently yields higher guarantee success rates and greater coverage than single-stage baselines across various candidate sizes and datasets.

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 Computation and Language.

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