@inproceedings{wang-etal-2025-rjag,
    title = "{RJAG}: Retrieval Judgment Augmented Generation",
    author = "Wang, Kuangzhi  and
      Huzhenhua, Huzhenhua  and
      Min, Ren  and
      Tao, Xiangzhi",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.73/",
    pages = "960--971",
    abstract = "``Large Language Models (LLMs) inevitably suffer from hallucinations, as relying solely on their parametric knowledge cannot guarantee the accuracy of generated content. To enhance text generation, retrieval-augmented generation (RAG) is proposed to incorporate external knowledge to achieve this. However, its effectiveness heavily depends on the relevance of retrieved documents, which poses a critical challenge: how to ensure the accuracy and reliability of model responses when retrieval results are inaccurate. Tackling this challenge, we propose RetrievalJudgment Augmented Generation (RJAG), a method that can enhance RAG through LLM-driven fine-grained relevance judgment mechanism and a task-adaptive knowledge combination strategy. RJAG judges and dynamically combines retrieved documents for both open-ended generation and closed-ended selection tasks. Additionally, large-scale web search is also included to expand the knowledge beyond static corpora. Experimental results on multiple bench-marks show that RJAG outperforms existing RAG methods, which will significantly enhance the accuracy and reliability while maintaining the system{'}s simplicity. Code is available at https://github.com/wangkz2023/RJAG.''"
}