@inproceedings{lou-etal-2026-less,
    title = "Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction",
    author = "Lou, Fanghao  and
      Wang, Qiqi  and
      Chen, Guanyu  and
      Zhang, Senbo  and
      Zhao, Kaiqi  and
      Liu, Qian  and
      Li, Huijia",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1450/",
    pages = "29015--29031",
    ISBN = "979-8-89176-395-1",
    abstract = "Legal case facts are often lengthy, complex, and difficult to process, posing challenges for legal judgment prediction. Although recent advances leverage large language models (LLMs) for legal reasoning, they face high computational costs and information degradation when handling long cases. Previous approaches, such as architectural modifications and text compression methods, reduce computational complexity to some extent but still struggle to effectively capture legally salient information in complex cases. We propose a legal knowledge{--}adaptive compression framework for long legal judgment prediction that integrates domain-specific legal knowledge to guide adaptive context compression. Our approach selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning. We evaluate the proposed framework on four real-world datasets spanning multiple jurisdictions and languages. Experimental results demonstrate that our method outperforms existing approaches in both prediction performance and computational efficiency."
}