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This research addresses a critical limitation in current LLMs by introducing a meta-cognitive framework that distinguishes between model confidence and actual accuracy. By partitioning knowledge into mastered, confused, and missing regions, the method enables targeted augmentation that reduces overconfident errors while fostering calibrated reasoning. The approach offers a practical pathway for improving reliability in knowledge-intensive applications without relying on simplistic performance metrics.
Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
arXiv:2602.12996v2 Announce Type: replace 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 fostering cognitive behaviors that better distinguish knowns from unknowns. All codes are available at https://github.com/AI9Stars/Know-More-Know-Clearer.
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 fostering cognitive behaviors that better distinguish knowns from unknowns. All codes are available at this https URL.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2602.12996 [cs.CL]
(or arXiv:2602.12996v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.12996 Focus to learn more arXiv-issued DOI via DataCite
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