用混合模型和追问机制提升法律求助分类准确率，低成本实现高精度匹配。
That's So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referral
- 融合大模型与机器学习模型，构建混合分类器提升性能。
- 在419个真实案例上达97.37%的命中率（hits@2），超越GPT-5。
- 适合法律援助系统优化，降低人工分诊成本。
每年数百万人通过热线、法律援助机构或律师推荐服务寻求法律帮助，首要任务是识别其法律问题类型。错误引导可能导致错过截止日期、遭受身体虐待、失去住房或子女监护权。本文提出并评估了FETCH分类器用于法律问题识别，采用两种提升准确率的方法：混合大模型与机器学习的集成分类方法，以及自动生成追问问题以丰富初始叙述。基于一个包含419个真实查询的非营利律师推荐服务数据集进行测试，最终在混合低成本模型下实现97.37%的分类准确率（hits@2），超过当前最先进的GPT-5模型。该方法在显著降低法律系统引导成本的同时，仍保持高准确率，具有重要应用前景。
原文摘要 · Abstract (English)
Each year millions of people seek help for their legal problems by calling a legal aid program hotline, walking into a legal aid office, or using a lawyer referral service. The first step to match them to the right help is to identify the legal problem the applicant is experiencing. Misdirection has consequences. Applicants may miss a deadline, experience physical abuse, lose housing or lose custody of children while waiting to connect to the right legal help. We introduce and evaluate the FETCH classifier for legal issue classification and describe two methods for improving accuracy: a hybrid LLM/ML ensemble classification method, and the automatic generation of follow-up questions to enrich the initial problem narrative. We employ a novel data set of 419 real-world queries to a nonprofit lawyer referral service. Ultimately, we show classification accuracy (hits@2) of 97.37\% using a mix of inexpensive models, exceeding the performance of the current state-of-the-art GPT-5 model. Our approach shows promise in significantly reducing the cost of guiding users of the legal system to the right resource for their problem while achieving high accuracy.
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