让大模型在答错题时能自觉识别并拒绝，避免盲目生成错误答案。
Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
- 用任务级自回归框架显式分离判断与生成，打破错误信息的干扰
- 在FaultyScience数据集上，错误回答率降低67%，同时保持原有推理能力
- 适合需要高可靠性、避免胡编乱造的智能问答系统使用
大语言模型常对有问题的输入生成看似合理但错误的答案，这并非因为缺乏知识——在判别式提示下，模型能识别问题，却无法在生成中体现该认知。这暴露了判别识别与生成行为间的根本性‘知行鸿沟’。本文构建了跨学科的大规模故障科学问题基准FaultyScience，发现该鸿沟普遍存在，根源在于词元级自回归机制将任务选择（验证或作答）与内容生成纠缠在一起。为此，提出DeIllusionLLM框架，通过自蒸馏在单一模型中统一判别判断与生成推理。实验表明，在自然提示下，该方法显著降低错误答案生成率（降幅67%），同时维持良好的通用推理性能，证明自蒸馏是弥合知行鸿沟的有效且可扩展的方案。
原文摘要 · Abstract (English)
LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify such issues, yet fail to reflect this in standard generative responses. This reveals a fundamental know-act gap between discriminative recognition and generative behavior. Prior work largely characterizes this issue in narrow settings, such as math word problems or question answering, with limited focus on how to integrate these two modes. In this work, we present a comprehensive analysis using FaultyScience, a newly constructed large-scale, cross-disciplinary benchmark of faulty scientific questions. We show that the gap is pervasive and stems from token-level autoregression, which entangles task selection (validate vs. answer) with content generation, preventing discriminative knowledge from being utilized. To address this, we propose DeIllusionLLM, a task-level autoregressive framework that explicitly models this decision. Through self-distillation, the model unifies discriminative judgment and generative reasoning within a single backbone. Empirically, DeIllusionLLM substantially reduces answer-despite-error failures under natural prompting while maintaining general reasoning performance, demonstrating that self-distillation is an effective and scalable solution for bridging the discriminative-generative know-act gap
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