**MetaRAG: Metacognitive Retrieval-Augmented Large Language Models** This paper introduces MetaRAG, a novel framework that combines retrieval-augmented generation with metacognition to enhance multi-hop reasoning in natural language processing. Traditional retrieval-augmented models often rely on single-time retrieval, which is insufficient for complex tasks requiring multi-hop reasoning. MetaRAG addresses this limitation by integrating metacognition, inspired by cognitive psychology, to enable the model to self-reflect and critically evaluate its cognitive processes. **Key Components:** 1. **Cognition Space:** Focuses on generating answers from questions and references. 2. **Metacognition Space:** Acts as an evaluator and critic, monitoring, evaluating, and planning the cognitive process. **Metacognitive Process:** 1. **Monitoring:** Assesses the quality of the current response to determine if metacognitive evaluation is needed. 2. **Evaluating:** Identifies reasons why the current answer may not meet requirements, using both declarative and procedural knowledge. 3. **Planning:** Develops tailored suggestions for improving the cognitive process based on the evaluation results. **Contributions:** - Introduces a metacognitive retrieval-augmented generation framework. - Identifies three primary challenges in multi-hop QA: insufficient knowledge, conflicting knowledge, and erroneous reasoning. - Proposes a three-step metacognitive regulation pipeline to address these challenges. **Experimental Results:** - MetaRAG outperforms existing baselines on two multi-hop question answering datasets (HotpotQA and 2WikiMultiHopQA). - The model demonstrates superior performance in handling conflicting knowledge and erroneous reasoning. **Conclusion:** MetaRAG enhances the accuracy and reliability of multi-hop reasoning in large language models by integrating metacognitive capabilities, making it a significant advancement in the field of natural language processing.**MetaRAG: Metacognitive Retrieval-Augmented Large Language Models** This paper introduces MetaRAG, a novel framework that combines retrieval-augmented generation with metacognition to enhance multi-hop reasoning in natural language processing. Traditional retrieval-augmented models often rely on single-time retrieval, which is insufficient for complex tasks requiring multi-hop reasoning. MetaRAG addresses this limitation by integrating metacognition, inspired by cognitive psychology, to enable the model to self-reflect and critically evaluate its cognitive processes. **Key Components:** 1. **Cognition Space:** Focuses on generating answers from questions and references. 2. **Metacognition Space:** Acts as an evaluator and critic, monitoring, evaluating, and planning the cognitive process. **Metacognitive Process:** 1. **Monitoring:** Assesses the quality of the current response to determine if metacognitive evaluation is needed. 2. **Evaluating:** Identifies reasons why the current answer may not meet requirements, using both declarative and procedural knowledge. 3. **Planning:** Develops tailored suggestions for improving the cognitive process based on the evaluation results. **Contributions:** - Introduces a metacognitive retrieval-augmented generation framework. - Identifies three primary challenges in multi-hop QA: insufficient knowledge, conflicting knowledge, and erroneous reasoning. - Proposes a three-step metacognitive regulation pipeline to address these challenges. **Experimental Results:** - MetaRAG outperforms existing baselines on two multi-hop question answering datasets (HotpotQA and 2WikiMultiHopQA). - The model demonstrates superior performance in handling conflicting knowledge and erroneous reasoning. **Conclusion:** MetaRAG enhances the accuracy and reliability of multi-hop reasoning in large language models by integrating metacognitive capabilities, making it a significant advancement in the field of natural language processing.