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Improved Confidence Estimates for Black-Box Large Language Models

A new arXiv preprint proposes a lightweight, dataset-aware method to improve confidence estimates for black-box LLMs by training simple classifiers on existing uncertainty scores and query similarity — aiming to increase reliability without requiring model access or labeled correctness data.