Test-time reasoning has become a significant field of study since the introduction of chain-of-thought reasoning in large language models (LLMs). However, the mechanisms of this reasoning process are still under-explored -- from the same input prompt, and even the same partial solution, LLMs can produce varied answers if sampled multiple times. We propose to leverage question-asking as an inference-time intervention that articulates information about the model's hidden state. To achieve that, we present a student-teacher setting where a student asks questions to a teacher. We train a probe on the student's hidden state before and after asking a question and find it is predictive of the trajectory's final correctness, even before generating the teacher's answer. This suggests there is a meaningful signal from the self-diagnosis that occurs during question generation rather than information transfer from the teacher. We then frame question-asking as a sequential decision problem, using this probe as a quality score, and define a gating policy to ask questions that maximize likelihood of correctness. We find that the success of question-asking as an intervention is largely dependent on the model's self-consistency. Our empirical results show a gap between detection and recovery; while our gating policy captures model correctness and uncertainty, interventions are equally likely to harm correct trajectories as they are to recover incorrect ones. This gap between diagnosis and correction has broader implications on language models' capacity for self-refinement under uncertainty.

翻译：自链式推理在大语言模型（LLMs）中引入以来，测试时推理已成为一个重要的研究领域。然而，这一推理过程的机制仍未被充分探索——面对相同的输入提示，甚至相同的部分解，多次采样时LLMs可能产生不同的答案。我们提出将提问作为一种推理时干预手段，用以阐明模型隐藏状态中的信息。为此，我们设计了一种师生设置，其中学生向教师提问。我们在学生提问前后对其隐藏状态进行探针训练，发现该探针能够预测生成轨迹的最终正确性，甚至在生成教师答案之前即可实现。这表明，问题生成过程中的自我诊断产生了有意义的信号，而非来自教师的信息传输。随后，我们将提问建模为序列决策问题，利用该探针作为质量评分，并定义一种门控策略，以选择能最大化正确性可能性的问题。我们发现，提问作为干预的成功与否在很大程度上取决于模型的自我一致性。我们的实证结果表明检测与恢复之间存在差距：尽管门控策略能够捕捉模型的正确性与不确定性，但干预在恢复错误轨迹的同时，也同等可能损害正确轨迹。这种诊断与修正之间的差距对语言模型在不确定性下的自我优化能力具有更广泛的启示。