Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLMs

• URL: http://arxiv.org/abs/2505.23845v1
• Date: Wed, 28 May 2025 17:01:30 GMT
• Title: Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLMs
• Authors: Jakub Podolak, Rajeev Verma,
• Abstract summary: We study the source of uncertainty in DeepSeek R1-32B by analyzing its self-reported verbal confidence on question answering (QA) tasks.<n>We show that granting DeepSeek the budget to explore its distribution by forcing a long chain-of-thought before the final answer greatly improves its verbal score effectiveness.
• Score: 3.2228025627337864
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We study the source of uncertainty in DeepSeek R1-32B by analyzing its self-reported verbal confidence on question answering (QA) tasks. In the default answer-then-confidence setting, the model is regularly over-confident, whereas semantic entropy - obtained by sampling many responses - remains reliable. We hypothesize that this is because of semantic entropy's larger test-time compute, which lets us explore the model's predictive distribution. We show that granting DeepSeek the budget to explore its distribution by forcing a long chain-of-thought before the final answer greatly improves its verbal score effectiveness, even on simple fact-retrieval questions that normally require no reasoning. Furthermore, a separate reader model that sees only the chain can reconstruct very similar confidences, indicating the verbal score might be merely a statistic of the alternatives surfaced during reasoning. Our analysis concludes that reliable uncertainty estimation requires explicit exploration of the generative space, and self-reported confidence is trustworthy only after such exploration.

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