面向大语言模型的推理时保形推理与有效事实性控制
Inference-Time Conformal Reasoning with Valid Factuality Control for Large Language Models
AI总结：
提出推理时保形推理框架，将保形预测集成到推理图生成中，通过图级不确定性校准生成停止阈值，实现有效事实性控制。
摘要：
大型语言模型（LLMs）越来越多地执行多步推理，其中中间声明形成隐式有向无环图，其节点正确性在结构上依赖于其祖先。这使得事实不确定性具有结构性，而非节点错误的简单累积，并且需要对推理结构进行推理时不确定性量化。虽然保形预测（CP）提供了灵活的用户指定事实性控制，但现有工作仍然是事后性的，无法在生成过程中进行干预。为了填补CP灵活性与事后局限性之间的差距，我们提出了一种推理时保形推理（ITCR）框架，该框架将CP直接集成到推理图生成中。ITCR学习一种结构级事实性不确定性函数，该函数在不进行复杂建模假设的情况下，聚合推理图上的声明级事实性信号。然后，我们基于图级事实性不确定性设计非一致性分数，并校准保形阈值以决定何时停止生成。我们从理论上证明这种生成是嵌套的，为事实性控制提供了有效的覆盖保证。在多个数据集和覆盖目标上的实验证明了经验上的有效覆盖。在下游推理任务中，推理时校准的图比事后剪枝的图产生更准确的生成。
英文摘要：
Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally conditioned on their ancestors. This makes factuality uncertainty structural, rather than a trivial accumulation of node-wise errors, and necessitates inference-time uncertainty quantification over the reasoning structure. While conformal prediction (CP) offers flexible user-specified factuality control, existing work remains post-hoc and cannot intervene during generation. To fill the gap between CP's flexibility and its post-hoc limitation, we propose an \emph{Inference-Time Conformal Reasoning (ITCR)} framework that integrates CP directly into reasoning graph generation. ITCR learns a structure-level factuality uncertainty function that aggregates claim-level factuality signals over reasoning graphs without complex modeling assumptions. We then design the non-conformity score based on graph-level factuality uncertainty and calibrate the conformal threshold to decide when to stop generation. We theoretically show such generation is nested, yielding valid coverage guarantees for factuality control. Experiments over multiple datasets and coverage objectives demonstrate empirically valid coverage. In downstream reasoning tasks, inference-time calibrated graphs yield more accurate generation than post-hoc pruned graphs.
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