Metacognitive Retrieval-Augmented Large Language Models
Yujia Zhou Zheng Liu Jiajie Jin Jian-Yun Nie Zhicheng Dou
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.