The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results. This paper presents an on-going use case developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aimed at adapting foundation models to SSH research practices and supporting tasks such as question answering, comparative document analysis and literature review. The evaluation framework follows the LLMs4EU protocol and encompasses both independent quantitative benchmarking (retrieval, summarisation, traceability and hallucination detection) and a qualitative assessment involving a panel of Digital Humanities experts. By embedding model adaptation within research infrastructures and a structured legal and ethical compliance framework, the use case explores how domain-sensitive and regulation-aware generative AI can support SSH scholarship while preserving reliability and epistemic responsibility.

Le projet RAPIDO vise à enrichir les publications scientifiques en reliant automatiquement les toponymes archéologiques à des référentiels d’autorité grâce à des outils de reconnaissance d’entités nommées. Il s’appuie sur l’annotation manuelle et l’apprentissage automatique (Flair, BERT) pour extraire et aligner ces toponymes. L’article présente cette méthode, les résultats obtenus et les perspectives d’amélioration.