让大模型学会区分已知与未知，提升知识增强的可靠性
Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
- 用内部认知信号划分知识区域，精准定位掌握、混淆和缺失部分
- 通过一致性机制校准模型自信度与实际准确率，减少盲目自信错误
- 适合需要高可信知识推理的场景，如医疗问答、科研辅助
知识增强显著提升了大语言模型在知识密集型任务中的表现。然而，现有方法通常假设模型性能等于其内在知识，忽视了知识与信心之间的差距，导致过度自信错误或不确定判断。为此，我们提出一种新型元认知框架，通过差异化干预与对齐实现可靠的知識增强。该方法利用内部认知信号将知识空间划分为已掌握、混淆和缺失三类区域，指导针对性的知识扩展。同时引入认知一致性机制，使主观确信度与客观准确性同步，确保知识边界的合理校准。大量实验表明，该框架持续优于多个强基线，验证了其在提升知识能力的同时，促进模型更理性地区分已知与未知的认知行为。代码已公开于 https://github.com/AI9Stars/Know-More-Know-Clearer。
原文摘要 · Abstract (English)
Knowledge augmentation has significantly enhanced the performance of Large Language Models (LLMs) in knowledge-intensive tasks. However, existing methods typically operate on the simplistic premise that model performance equates with internal knowledge, overlooking the knowledge-confidence gaps that lead to overconfident errors or uncertain truths. To bridge this gap, we propose a novel meta-cognitive framework for reliable knowledge augmentation via differentiated intervention and alignment. Our approach leverages internal cognitive signals to partition the knowledge space into mastered, confused, and missing regions, guiding targeted knowledge expansion. Furthermore, we introduce a cognitive consistency mechanism to synchronize subjective certainty with objective accuracy, ensuring calibrated knowledge boundaries. Extensive experiments demonstrate the our framework consistently outperforms strong baselines, validating its rationality in not only enhancing knowledge capabilities but also fostering cognitive behaviors that better distinguish knowns from unknowns. All codes are available at https://github.com/AI9Stars/Know-More-Know-Clearer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。