Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
A new paper presents a method for improving the reliability of large language models (LLMs) in judgment tasks. The authors propose a margin-adaptive confidence ranking system that enhances the relationship between model confidence and human agreement. This approach aims to address limitations in existing confidence estimators and improve accuracy across various datasets.
- ▪The paper introduces a margin-adaptive confidence ranking for LLMs.
- ▪It addresses issues with existing confidence estimators by learning a dedicated confidence estimator.
- ▪The proposed method improves ranking accuracy and strengthens the relationship between confidence and disagreement risk.
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Computer Science > Machine Learning arXiv:2605.15416 (cs) [Submitted on 14 May 2026] Title:Margin-Adaptive Confidence Ranking for Reliable LLM Judgement Authors:Gaojie Jin, Yong Tao, Lijia Yu, Tianjin Huang View a PDF of the paper titled Margin-Adaptive Confidence Ranking for Reliable LLM Judgement, by Gaojie Jin and 3 other authors View PDF HTML (experimental) 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.
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