推理模型的思考长度可作可靠置信度信号，有效缓解幻觉问题。
Trace Length is a Simple Uncertainty Signal in Reasoning Models
- 用推理过程长度衡量模型置信度，无需额外训练。
- 在多模型、多数据集上表现优于或互补于其他零样本方法。
- 发现高熵分叉令牌是关键机制，适合部署阶段使用。
大语言模型的不确定性量化是解决幻觉等问题、提升可靠部署的关键方向。本文证明，推理轨迹长度是一种简单且有效的置信度估计器。在多个模型、数据集和提示下进行的综合实验表明，轨迹长度的表现与口头置信度等零样本估计器相当且互补。研究发现，推理后训练从根本上改变了轨迹长度与准确率的关系，超越了以往仅观察到的‘过度思考’导致轨迹变长的现象。通过控制问题难度和GRPO引起的长度偏差等混淆因素，仍能观察到该效应。我们识别出高熵或‘分叉’令牌在其中起核心作用。结果表明，推理后训练提升了超出言语表达的不确定性量化能力，并确立了轨迹长度作为大型推理模型实用置信度度量的可行性。
原文摘要 · Abstract (English)
Uncertainty quantification for LLMs is a key research direction towards addressing hallucination and other issues that limit their reliable deployment. In this work, we show that reasoning trace length is a simple and useful confidence estimator in large reasoning models. Through comprehensive experiments across multiple models, datasets, and prompts, we show that trace length performs in comparable but complementary ways to other zero-shot confidence estimators such as verbalized confidence. Our work reveals that reasoning post-training fundamentally alters the relationship between trace length and accuracy, going beyond prior work that had shown that post-training causes traces to grow longer in general (e.g., "overthinking"). We investigate the mechanisms behind trace length's performance as a confidence signal, observing that the effect remains even after adjusting for confounders such as problem difficulty and GRPO-induced length bias. We identify high-entropy or "forking" tokens as playing a key role in the mechanism. Our findings demonstrate that reasoning post-training enhances uncertainty quantification beyond verbal expressions, and establish trace length as a practical confidence measure for large reasoning models.
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