@inproceedings{chu-etal-2025-self,
    title = "Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering",
    author = "Chu, Zheng  and
      Fan, Huiming  and
      Chen, Jingchang  and
      Wang, Qianyu  and
      Yang, Mingda  and
      Liang, Jiafeng  and
      Wang, Zhongjie  and
      Li, Hao  and
      Tang, Guo  and
      Liu, Ming  and
      Qin, Bing",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.123/",
    doi = "10.18653/v1/2025.findings-acl.123",
    pages = "2415--2438",
    ISBN = "979-8-89176-256-5",
    abstract = "Although large language models (LLMs) have demonstrated remarkable reasoning capabilities, they still face challenges in knowledge-intensive multi-hop reasoning. Recent work explores iterative retrieval to address complex problems. However, the absence of intermediate guidance often leads to inaccurate retrieval and intermediate reasoning errors, leading to incorrect reasoning. To address these, we propose Self-Critique Guided Iterative Reasoning (SiGIR), which uses self-critique feedback to guide the iterative reasoning process. Specifically, through end-to-end training, we enable the model to iteratively address complex problems via question decomposition, while also being able to self-evaluate its intermediate reasoning steps. During iterative reasoning, the model engages in branching exploration and employs self-evaluation to guide the selection of promising reasoning trajectories. Extensive experiments on three multi-hop reasoning datasets demonstrate the effectiveness of our proposed method, surpassing the previous SOTA by 8.6{\%}. Furthermore, our thorough analysis offers insights for future research. Our code, data, and models are available at https://github.com/zchuz/SiGIR-MHQA."
}