Metacognitive Retrieval-Augmented Large Language Models

• URL: http://arxiv.org/abs/2402.11626v1
• Date: Sun, 18 Feb 2024 15:41:31 GMT
• Title: Metacognitive Retrieval-Augmented Large Language Models
• Authors: Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, Zhicheng Dou
• Abstract summary: This paper introduces MetaRAG, an approach that combines the retrieval-augmented generation process with metacognition. By integrating this, MetaRAG enables the model to monitor, evaluate, and plan its response strategies. Empirical evaluations show that MetaRAG significantly outperforms existing methods.
• Score: 43.57020180706832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation have become central in natural language processing due to their efficacy in generating factual content. While traditional methods employ single-time retrieval, more recent approaches have shifted towards multi-time retrieval for multi-hop reasoning tasks. However, these strategies are bound by predefined reasoning steps, potentially leading to inaccuracies in response generation. This paper introduces MetaRAG, an approach that combines the retrieval-augmented generation process with metacognition. Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes. By integrating this, MetaRAG enables the model to monitor, evaluate, and plan its response strategies, enhancing its introspective reasoning abilities. Through a three-step metacognitive regulation pipeline, the model can identify inadequacies in initial cognitive responses and fixes them. Empirical evaluations show that MetaRAG significantly outperforms existing methods.

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