发表机构
Talan（塔兰公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究法国国际招聘的法律框架，通过公开基准比较参数化语言模型基线与密集检索增强在两种模型规模下对52个合成档案的效果，发现检索能改进行政指导，凸显检索基础的重要性，推动混合检索策略研究。
AI 中文摘要
在法国进行国际招聘需要应对现有法律人工智能基准中缺乏的分层法律框架。我们为此领域提供了一个公开可用的基准和首次比较评估，涵盖许可类型推荐、所需文件检索和法律引用覆盖范围。在52个带注释的合成档案上，将参数化语言模型基线与两种模型规模（Qwen3.5 - 9B和 - 27B）的密集检索增强进行比较，发现检索在两个规模上都改进了行政指导，尤其是许可类型准确性。结果证实检索基础对该领域更可靠的行政指导很重要，并促使进一步研究混合检索策略。
英文摘要
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.
Journal refInternational workshop on AI for Human Resources and Public Employment Services (ECML-PKDD 2026)
