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    <abstract>Retrieval-augmented generation (RAG) has emerged as a pivotal technology in natural language processing, owing to its efficacy in generating factual content. However, its informative inputs and complex paradigms often lead to a greater variety of errors. Consequently, achieving automated on-policy assessment and error-oriented correction remain unresolved issues. In this paper, we propose RAG-Critic, a novel framework that leverages a critic-guided agentic workflow to improve RAG capabilities autonomously. Specifically, we initially design a data-driven error mining pipeline to establish a hierarchical RAG error system. Based on this system, we progressively align an error-critic model using a coarse-to-fine training objective, which automatically provides fine-grained error feedback. Finally, we design a critic-guided agentic RAG workflow that customizes executor-based solution flows based on the error-critic model’s feedback, facilitating an error-driven self-correction process. Experimental results across seven RAG-related datasets confirm the effectiveness of RAG-Critic, while qualitative analysis offers practical insights for achieving reliable RAG systems. Our dataset and code are available at https://github.com/RUC-NLPIR/RAG-Critic.</abstract>
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%0 Conference Proceedings
%T RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation
%A Dong, Guanting
%A Jin, Jiajie
%A Li, Xiaoxi
%A Zhu, Yutao
%A Dou, Zhicheng
%A Wen, Ji-Rong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F dong-etal-2025-rag
%X Retrieval-augmented generation (RAG) has emerged as a pivotal technology in natural language processing, owing to its efficacy in generating factual content. However, its informative inputs and complex paradigms often lead to a greater variety of errors. Consequently, achieving automated on-policy assessment and error-oriented correction remain unresolved issues. In this paper, we propose RAG-Critic, a novel framework that leverages a critic-guided agentic workflow to improve RAG capabilities autonomously. Specifically, we initially design a data-driven error mining pipeline to establish a hierarchical RAG error system. Based on this system, we progressively align an error-critic model using a coarse-to-fine training objective, which automatically provides fine-grained error feedback. Finally, we design a critic-guided agentic RAG workflow that customizes executor-based solution flows based on the error-critic model’s feedback, facilitating an error-driven self-correction process. Experimental results across seven RAG-related datasets confirm the effectiveness of RAG-Critic, while qualitative analysis offers practical insights for achieving reliable RAG systems. Our dataset and code are available at https://github.com/RUC-NLPIR/RAG-Critic.
%R 10.18653/v1/2025.acl-long.179
%U https://aclanthology.org/2025.acl-long.179/
%U https://doi.org/10.18653/v1/2025.acl-long.179
%P 3551-3578

Markdown (Informal)

[RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation](https://aclanthology.org/2025.acl-long.179/) (Dong et al., ACL 2025)

• RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation (Dong et al., ACL 2025)

ACL

• Guanting Dong, Jiajie Jin, Xiaoxi Li, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. 2025. RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3551–3578, Vienna, Austria. Association for Computational Linguistics.