追踪语言模型推理中的不确定性
Tracing Uncertainty in Language Model "Reasoning"
AI总结：
研究通过不确定性量化分析语言模型推理轨迹，发现其不确定性特征可预测最终答案正确性，且早期即可检测错误。
摘要：
语言模型（LM）的'推理'，通常描述为链式思维或测试时扩展，常提升基准性能，但其动态机制尚不明确。本文通过将推理轨迹视为演化的模型状态，利用不确定性轨迹特征（如斜率和线性度）预测最终答案正确性，五种模型在GSM8K和ProntoQA上的AUROC达0.807，且仅用前几百年token即可检测错误，正确轨迹的不确定性下降更陡且非线性，表明基于不确定性决策的方法为研究LM推理过程提供了原理性视角。
英文摘要：
Language model (LM) "reasoning", commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics underlying this process remain poorly understood. We study these dynamics through the lens of uncertainty quantification by treating the "reasoning" traces, the intermediate token sequences generated by LMs, as evolving model states. We summarize each trace by an uncertainty trace profile: a small set of features describing the shape of the uncertainty signal over its trace, such as its slope and linearity. We find that across five LMs evaluated on GSM8K and ProntoQA, these profiles predict whether a trace yields a correct final answer with AUROC up to 0.807, improving markedly on recent related work. We reach AUROC 0.801 using only the first few hundred tokens of full traces, suggesting that errors can be detected early in the generation. A detailed comparison of correct and incorrect traces further reveals qualitatively distinct uncertainty profiles, with correct traces showing a steeper and less linear decline in uncertainty. Together, the results suggest that our method, grounded in decision-making under uncertainty, provides a principled lens for studying the generative process underlying LM "reasoning".
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
