评测法语移民法RAG效果，发现检索增强能显著提升审批建议准确率。
Evaluating RAG for French immigration law: a benchmark and baseline study
- 构建首个法语移民法RAG基准，涵盖签证推荐、材料检索等任务
- 在52个合成案例上，检索增强使签证类型预测准确率明显提升
- 适合法律AI研究者和政策自动化开发者参考
法国国际招聘需应对复杂的法律框架，现有法律AI基准未覆盖此领域。我们提出首个公开可用的基准与首次对比评估，涵盖许可类型推荐、必要文件检索及法律引用覆盖率。在52个标注的合成个人档案上，对比了Qwen3.5-9B与-27B两个规模的参数化大模型基线与密集检索增强的效果，结果表明检索增强在两个模型规模下均提升了行政指导质量，尤其显著改善了许可类型预测准确率。结果证实，检索增强对提高该领域行政指导可靠性至关重要，并推动对混合检索策略的进一步研究。
原文摘要 · Abstract (English)
International recruitment in France requires navigating a layered legal framework absent from existing legal AI benchmarks. We present a publicly available benchmark and first comparative evaluation for this domain, covering permit-type recommendation, required-document retrieval, and legal citation coverage. Comparing a parametric LLM baseline against dense retrieval augmentation at two model scales (Qwen3.5-9B and -27B) on 52 annotated synthetic profiles, we find that retrieval improves administrative guidance at both scales, most notably permit-type accuracy. Our results confirm that retrieval grounding is important for more reliable administrative guidance in this domain, and motivate further investigation of hybrid retrieval strategies.
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