Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models

2026-02-13

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationSearch and Optimization: Metareasoning and MetaheuristicsData Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• knowledge augmentation
• large language models
• knowledge-confidence gap
• overconfident errors
• cognitive calibration

Innovation

• meta-cognitive framework
• knowledge augmentation
• cognitive consistency
• confidence calibration
• knowledge space partitioning