@inproceedings{jiao-etal-2025-hirag,
    title = "{HIRAG}: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation",
    author = "Jiao, Yihan  and
      Tan, Zhehao  and
      Yang, Dan  and
      Sun, Duolin  and
      Feng, Jie  and
      Shen, Yue  and
      Wang, Jian  and
      Wei, Peng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.274/",
    doi = "10.18653/v1/2025.findings-emnlp.274",
    pages = "5111--5130",
    ISBN = "979-8-89176-335-7",
    abstract = "Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-specific problems. Traditional RAG systems primarily rely on the in-context learning (ICL) capabilities of the large language model itself. Still, in-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to challenges with inconsistent document quality and retrieval system imperfections. Even the limited studies that fine-tune RAG generative models often lack a granular focus on RAG tasks or a deeper utilization of chain-of-thought processes. To address this, we propose that RAG models should possess three progressively hierarchical abilities (1) Filtering: the ability to select relevant information; (2) Combination: the ability to combine semantic information across paragraphs; and (3) RAG-specific reasoning: the ability to further process external knowledge using internal knowledge. Thus, we introduce our new RAG instruction fine-tuning method, Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (HIRAG) incorporates a ``think before answering'' strategy. This method enhances the model{'}s open-book examination capability by utilizing multi-level progressive chain-of-thought. Experiments show that the HIRAG training strategy significantly improves the model{'}s performance on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA."
}