Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
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Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
arXiv:2605.15416v2 Announce Type: replace Abstract: Jung et al. (2025) introduce a hypothesis testing framework for guaranteeing agreement between large language models (LLMs) and human judgments, relying on the assumption that the model's estimated confidence is monotonic with respect to human-disagreement risk. In practice, however, this assumption may be violated, and the generalization behavior of the confidence estimator is not explicitly analyzed.