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[2602.12996] Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
Summary
This article presents a novel meta-cognitive framework aimed at enhancing knowledge augmentation in Large Language Models (LLMs), addressing knowledge-confidence gaps that lead to errors.
Why It Matters
As LLMs become increasingly integral in various applications, improving their knowledge accuracy and reliability is crucial. This framework not only enhances performance but also fosters better cognitive behaviors, which is essential for applications requiring high trust and accuracy.
Key Takeaways
- Introduces a meta-cognitive framework for knowledge augmentation in LLMs.
- Addresses knowledge-confidence gaps to reduce overconfident errors.
- Utilizes cognitive signals to improve knowledge partitioning and targeted expansion.
- Implements a cognitive consistency mechanism for aligning certainty with accuracy.
- Demonstrates superior performance compared to existing methods through extensive experiments.
Computer Science > Computation and Language arXiv:2602.12996 (cs) [Submitted on 13 Feb 2026] Title:Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models Authors:Hao Chen, Ye He, Yuchun Fan, Yukun Yan, Zhenghao Liu, Qingfu Zhu, Maosong Sun, Wanxiang Che View a PDF of the paper titled Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models, by Hao Chen and 7 other authors View PDF Abstract: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 fost...