Metacognitive Retrieval-Augmented Large Language Models
<|reference_start|>Metacognitive Retrieval-Augmented Large Language Models: 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.<|reference_end|>
@article{zhou2024metacognitive,
    title={Metacognitive Retrieval-Augmented Large Language Models},
    author={Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, Zhicheng Dou},
    journal={arXiv preprint arXiv:2402.11626},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11626},
    primaryClass={cs.CL cs.IR}
}