@inproceedings{tang-etal-2025-comprehensive,
    title = "A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment",
    author = "Tang, Quanwei  and
      Lee, Sophia Yat Mei  and
      Wu, Junshuang  and
      Zhang, Dong  and
      Li, Shoushan  and
      Cambria, Erik  and
      Zhou, Guodong",
    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/acl25-workshop-ingestion/2025.findings-acl.1108/",
    pages = "21504--21523",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent advancements in retrieval-augmented generation (RAG) have enhanced large language models in question answering by integrating external knowledge. However, challenges persist in achieving global understanding and aligning responses with human ethical and quality preferences. To address these issues, we propose GraphMPA, a comprehensive graph-based framework with mode-seeking preference alignment. Our approach constructs a hierarchical document graph using a general similarity measurement, mimicking human cognitive processes for information understanding and synthesis. Additionally, we introduce mode-seeking preference optimization to better align model outputs with human preferences through probability-matching constraints. Extensive experiments on six datasets demonstrate the effectiveness of our GraphMPA."
}