From token probabilities to calibrated confidence: An empirical study of mathematical question answering

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arXiv:2608.07827v1 Announce Type: new Abstract: Confidence estimation for large language models (LLMs) aims to estimate the probability that a generated answer is correct, while calibration aligns these estimates with empirical accuracy. Prior work has shown that token probabilities are often overconfident, we investi…