用大模型辅助法律援助申请初筛，提升效率并减少漏判。
Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models
- 结合规则逻辑与大模型进行自动化资格判断
- 最佳模型F1达0.82，且有效降低误排除率
- 适合法律科技、公益服务及司法改革领域参考
法律援助的申请初筛过程耗时耗力，主要因资格标准复杂多变，需频繁更新。本文研究利用大语言模型（LLMs）缓解这一负担，设计了一个融合逻辑规则与大模型的数字初筛平台，并评估了8种不同大模型在资格推荐任务中的表现。结果表明该方法具有显著潜力，能有效缩小司法可及性差距；其中最优模型取得0.82的F1分数，同时最大限度减少了假阴性情况，为自动化法律援助初筛提供了可行方案。
原文摘要 · Abstract (English)
Legal intake, the process of finding out if an applicant is eligible for help from a free legal aid program, takes significant time and resources. In part this is because eligibility criteria are nuanced, open-textured, and require frequent revision as grants start and end. In this paper, we investigate the use of large language models (LLMs) to reduce this burden. We describe a digital intake platform that combines logical rules with LLMs to offer eligibility recommendations, and we evaluate the ability of 8 different LLMs to perform this task. We find promising results for this approach to help close the access to justice gap, with the best model reaching an F1 score of .82, while minimizing false negatives.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。