为公设辩护律师开发智能检索工具，提升法律研究效率。
Legal Retrieval for Public Defenders
- 结合法律推理扩展查询，利用领域数据与合成样例提升检索质量。
- 新基准与专业辩护人标注数据高度相关，验证了有效性。
- 开源查询分类与标注数据集，助力公益法律AI研究。
AI工具被提议用于缓解公共机构的工作负担。在公共辩护领域——这一宪法赋予辩护权、法律复杂性高、案件积压严重且资源有限的场景中，实践者面临巨大压力。然而，目前缺乏AI如何切实支持辩护律师日常工作的证据。我们与新泽西州公设辩护办公室合作，开发了NJ BriefBank，一个可自动推送相关上诉状的检索工具，以简化法律研究与文书撰写。研究发现，现有检索基准无法有效迁移至真实的公共辩护检索任务；但引入领域知识后，检索质量显著提升，包括通过法律推理扩展查询、使用领域特定数据及精心设计的合成示例。为推动后续研究，我们发布了真实辩护人搜索查询的分类体系，以及人工标注的评估数据集。该基准与由资深辩护人标注的专有数据集高度相关。本工作改进了现实法律检索评估的现状，并展示了将AI应用于实际公益场景的一种可行路径。
原文摘要 · Abstract (English)
AI tools are suggested as solutions to assist public agencies with heavy workloads. In public defense -- where a constitutional right to counsel meets the complexities of law, overwhelming caseloads, and constrained resources -- practitioners face especially taxing conditions. Yet, there is little evidence of how AI could meaningfully support defenders' day-to-day work. In partnership with the New Jersey Office of the Public Defender, we develop the NJ BriefBank, a retrieval tool which surfaces relevant appellate briefs to streamline legal research and writing. We show that existing retrieval benchmarks fail to transfer to real public defense research, however adding domain knowledge improves retrieval quality. This includes query expansion with legal reasoning, domain-specific data and curated synthetic examples. To facilitate further research, we release a taxonomy of realistic defender search queries and a manually annotated evaluation dataset for public defense retrieval. This benchmark is highly correlated with a proprietary retrieval dataset annotated by experienced public defenders. Our work improves on the status quo of realistic legal retrieval benchmarking and illustrates one approach to applying AI in a real-world public interest setting.
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