Identifying Influential N-grams in Confidence Calibration via Regression Analysis

ArXi:2604.05757v1 Announce Type: new While large language models (LLMs) improve performance by explicit reasoning, their responses are often overconfident, even though they include linguistic expressions nstrating uncertainty. In this work, we identify what linguistic expressions are related to confidence by applying the regression method. Specifically, we predict confidence of those linguistic expressions in the reasoning parts of LLMs as the dependent variables and analyze the relationship between a specific $n$-gram and confidence.