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Evaluating RAG for French immigration law: a benchmark and baseline study

Primary research

#874

T1new

Canonical URL: http://arxiv.org/abs/2607.24449v1
Topic: unassigned (set during synthesis)
First seen: 2026-07-28 07:16:45
Last seen: 2026-07-28 07:16:45

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arXiv2026-07-28 07:16:00
Evaluating RAG for French immigration law: a benchmark and baseline study
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.