Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLM s (2025)

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. Our analysis concludes that reliable uncertainty estimation requires explicit exploration of the generative space, and self-reported confidence is trustworthy only after such exploration.

## introduction generative language models (glms) like gpt, llama, or deepseek families have achieved great performance on diverse tasks @xcite ), yet they are prone to failure modes such as "hallucinations" @xcite . these inaccuracies can undermine trust and lead to poor decisions in llm-assisted systems @xcite . to mitigate this issue, quantification and the communication of model's uncertainty in its outputs is seen as a potential to entrust these models with reliability. numerous uncertainty-quantification (uq) approaches have been proposed in this direction: from monte-carlo sampling based, such as semantic entropy (se) @xcite to simpler verbalized confidence estimation (vc), which just asks the model directly to state its confidence @xcite . while verbalized confidence estimation is easy to use, there is no scientific consensus on what it represents or what its source is, potentially making it unreliable to use in critical scenarios. furthermore, prior work has shown that verbalized confidence is often pathological -for the same question a model might first return "answer a (100 % confidence)" and, in a second sample, "answer b (95 % confidence)," even though these probabilities cannot coexist @xcite . on a more practical side, verbalized scores may be over-confident @xcite , whereas semantic entropy remains comparatively well calibrated @xcite . semantic entropy's effectiveness can be attributed to its test-time compute-allocating extra tokens at inference @xcite to explore the predictive distribution. test-time compute can also come in the form of an extended reasoning chain that precedes the final answer @xcite , and most recent works show that such reasoning can improve verbalized or token-level calibration as well @xcite . these findings prompted us to pose a hypothesis: is the model able to directly quantify and express verbally its uncertainty, or is the test-time token exploration necessary for the model to reliably summarize its confidence? in this paper, we try to answer this question and better understand the source of vc by performing a set of experiments with deepseek r1-32b @xcite as a representative model. our results show that without any chain-ofthought, deepseek's verbalized scores carry little information about correctness. as we grant the model progressively larger reasoning budgets, its calibration improves and approaches the reliability of semantic entropy, even on simple fact-retrieval items. this trend suggests that meaningful uncertainty estimates emerge only after the model's predictive space has been explored, and that the final confidence percentage largely summarizes the diversity exposed in this process. we further enforce this hypothesis by using a separate reader model that, by just analyzing deepseek's reasoning trace, matches the reliability of deepseek's own verbalized confidence. ## background and related work generative language models frequently generate fluent but incorrect answers that can cause downstream harm @xcite . when no external verifier is available, a model's self-reported confidence is the only proxy for correctness, making reliable uncertainty estimates essential. ## methodology our objective is to uncover where a model's verbalized confidence comes from. we identify two competing views: intrinsic latent variable: the model can read out a hidden latent belief state and use it to express its uncertainty, and self-sampling: model does not have access to any reliable latent source of confidence, and reliable confidence emerges only after the model explicitly explores its own predictive space, as semantic entropy does by sampling many answers. we test these views through a set of experiments that measure the behavior of vc when the model is forced to reason before answering, compare the effectiveness and accuracy to the se, and analyze the uncertainty exposed in the reasoning traces. we describe our experimental setup in appendix a. ## results without any reasoning tokens, the score is barely better than random, and with enough exploration budget, vc can approach se's effectiveness (subsection 4.1). furthermore, an external reader can recover essentially the same uncertainty signal by inspecting the chain of thought alone (subsection 4.2), suggesting that the self-sampling hypothesis might be true. ## discussion and future work we aimed to determine whether large language models can directly verbalize well-calibrated uncertainty or whether reliable confidence estimates only emerge after explicit exploration of their predictive space, via additional test-time compute such as parallel sampling (semantic entropy) or extended reasoning. our experiments suggest that test-time compute, not the particular uncertainty heuristic, is the decisive factor for obtaining reliable confidence estimates in deepseek-r1-32b. left to produce only a short answer, the model remains over-confident because its belief state cannot be accessed directly. granting the model additional tokens, either by sampling independent continuations (semantic entropy) or by forcing a longer chain of thought, al-lows it to externalize alternative hypotheses. this exposes a big issue with verbalized confidenceits appeal lies in the simplicity and how fast it is, yet it works well only after a significant reasoning computation is done. while these results are encouraging, they may not be generalizable since we've tested only one model and used a very compact qa dataset. furthermore, assuming it is true that test-time compute is the decisive factor for reliable uq, it still might be the case that some methods of test-time compute may be more efficient in eliciting uncertainty than others. future work could focus on making models reason more efficiently or explore their uncertainty in a more structured way. that could help verbalized confidence inherit the semantic-entropy-level of calibration with less computation needed. xinran zhao, hongming zhang, xiaoman pan, wenlin yao, dong yu, tongshuang wu, and jianshu chen. 2024. fact-and-reflection (far) improves confidence calibration of large language models. in findings of the association for computational linguistics: acl 2024, pages 8702-8718, bangkok, thailand. association for computational linguistics. system prompt: (...) please answer user's questions. provide both the final answer and your confidence that the answer is correct. (...) before the final answer, you might be asked to provide a short reasoning between `think` tokens. (...) user message: where was charles babbage born? verbalized confidence with forced reasoning semantic entropy final answer: cambridge, confidence: 80% <@xmath0, let me think … cambridge. wait, lots of scientists … london? wait, let's … so final answer: cambridge, uk confidence: … final answer: london confidence: … final answer: cambridge confidence: … … cambridge: 5 london: 2 most frequent answer: cambridge entropy: 0.86 🐋deepseek 🐋deepseek fixed reasoning budget (e.g. 500 tokens) ⏩add "wait," and continue 👈223 tokens 👈245 tokens 👈32 tokens 1 2 10 ⏩add "wait," and continue