@inproceedings{ran-etal-2025-bisaga,
    title = "{B}i{S}a{GA}: A Novel Bidirectional Sparse Graph Attention Adapter for Evidence-Based Fact-Checking",
    author = "Ran, Junfeng  and
      Luo, Weiyao  and
      Tian, Zailong  and
      Zhao, Guangxiang  and
      Zhu, Dawei  and
      Wu, Longyun  and
      Huang, Hailiang  and
      Li, Sujian",
    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-1.72/",
    pages = "946--959",
    abstract = "``Evidence-based fact-checking aims to verify or debunk claims using evidence and has greatly benefited from advancements in Large Language Models (LLMs). This task relies on clarify-ing and discriminating relations between entities. However, autoregressive LLMs struggle with understanding relations presented in different orders or narratives, as their unidirectional na-ture hampers effective performance. To address this challenge, we propose a novel method that leverages bidirectional attention as an external adapter to facilitate two-way information aggregation. Additionally, we employ hierarchical sparse graphs to merge local and global information and introduce an efficient feature-compression technique to minimize the number of adapter parameters. Experimental results on both English and Chinese datasets demonstrate the significant improvements achieved by our approach, showcasing state-of-the-art performance in the evidence-based fact-checking task.''"
}