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.

翻译：检索增强生成因其在生成事实性内容方面的有效性，已成为自然语言处理领域的核心方法。传统方法采用单次检索，而近期方法在多跳推理任务中转向了多次检索。然而，这些策略受限于预定义的推理步骤，可能导致响应生成中的不准确性。本文提出MetaRAG方法，将检索增强生成过程与元认知相结合。借鉴认知心理学，元认知使实体能够自我反思并批判性评估其认知过程。通过整合这一机制，MetaRAG使模型能够监控、评估和规划其响应策略，从而增强其内省推理能力。通过三步元认知调节流程，模型能够识别初始认知响应中的不足并加以修正。实验评估表明，MetaRAG显著优于现有方法。