When Linguistic and Internal Confidence Diverge in Large Language Models

2026-08-28

AI Summary

研究探讨了大型语言模型的语义自信与内部自信之间的差异，通过分类和生成任务测试两者关联性，并提出应在使用前进行多轴诊断评估。

Abstract

Users often ask large language models (LLMs) to report how confident they are, but it is unclear whether such linguistic confidence tracks the model's internal confidence. We study this question across 8 classification tasks, 2 generation tasks and 30 models from three families. For classification, we compare linguistic confidence with logits-based confidence along three axes: association, magnitude agreement and calibration. For generation, we test whether linguistic confidence tracks semantic-entropy-based uncertainty. The axes frequently diverge. Instance-level association is weak on average, although it improves on easier items and for stronger base models. Instruction-tuned models often report higher confidence and sometimes show higher association, but they also have larger confidence gaps and worse calibration. Prompt design mostly changes the distribution of reported confidence. Attitude cues inflate confidence without improving alignment, while score exemplars can preserve rank-order signal when they avoid collapsed confidence values. Regression analyses show that distributional properties of confidence scores explain much of the observed alignment pattern, with model metadata playing a smaller role after controls. These results support a lossy-channel view of linguistic confidence. A more dispersed verbal confidence distribution can carry useful rank information, but it does not make the scores calibrated. Linguistic confidence should therefore be evaluated with multi-axis diagnostics before being used in downstream reliability pipelines.

Problem

• Linguistic Confidence
• Internal Confidence
• Large Language Models
• Classification Tasks
• Generation Tasks

Innovation

• linguistic confidence
• internal confidence
• large language models
• confidence calibration
• multi-axis diagnostics