《叩开大门：利用大语言模型简化民事法律服务的受理流程》
Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models
AI总结：
针对民事法律援助受理流程耗时长、资格标准调整频繁的问题，研究提出结合逻辑规则与大语言模型的数字化受理平台，测试8种模型后发现最优模型F1达0.82且假阴性低，可助力缩小司法资源获取差距。
摘要：
法律援助受理是确定申请人是否符合免费法律援助项目帮扶资格的流程，会耗费大量时间与资源。这在一定程度上是因为资格标准细致复杂、表述具有开放性，且会随着资助项目的启动与终止频繁调整。本文研究了利用大语言模型（LLMs）减轻这一负担的可行性。我们介绍了一个将逻辑规则与LLMs相结合、提供资格评估建议的数字化受理平台，并评估了8种不同LLM执行该任务的能力。研究发现该方法在填补司法资源获取缺口方面具有可观前景，最优模型的F1分数可达0.82，同时能将假阴性率控制在较低水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
