Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
<|reference_start|>Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation: Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) by incorporating extensive knowledge retrieved from external sources. However, such approach encounters some challenges: Firstly, the original queries may not be suitable for precise retrieval, resulting in erroneous contextual knowledge; Secondly, the language model can easily generate inconsistent answer with external references due to their knowledge boundary limitation. To address these issues, we propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency. Specifically, we integrate the verification module into the RAG, engaging in scoring, judgment, and rewriting. To correct external retrieval errors, CoV-RAG retrieves new knowledge using a revised query. To correct internal generation errors, we unify QA and verification tasks with a Chain-of-Thought (CoT) reasoning during training. Our comprehensive experiments across various LLMs demonstrate the effectiveness and adaptability compared with other strong baselines. Especially, our CoV-RAG can significantly surpass the state-of-the-art baselines using different LLM backbones.<|reference_end|>
@article{he2024retrieving,,
    title={Retrieving, Rethinking and Revising: The Chain-of-Verification Can
  Improve Retrieval Augmented Generation},
    author={Bolei He, Nuo Chen, Xinran He, Lingyong Yan, Zhenkai Wei, Jinchang
  Luo, Zhen-Hua Ling},
    journal={arXiv preprint arXiv:2410.05801},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05801},
    primaryClass={cs.CL cs.AI}
}