Study analyzes medical LLM adaptation trade-offs in French QA

A new study published on arXiv explores the effectiveness of different adaptation strategies for large language models (LLMs) in the medical domain, specifically using French medical question-answering (QA) as a case study. Researchers compared continual pretraining (CPT), supervised fine-tuning (SFT), and a combination of both, evaluating their impact on multiple-choice (MCQA) and open-ended QA (OEQA). The findings suggest that for MCQA, CPT+SFT often yields the best results, though SFT alone is a cost-effective alternative. For OEQA, CPT improves overlap-based metrics, while SFT can degrade quality; instruction tuning and CPT+SFT are preferred by LLM-based evaluations. The study also demonstrated effective cross-lingual transfer from French adaptation to English benchmarks, offering practical guidance for selecting adaptation strategies under computational constraints. AI

IMPACT Provides practical guidelines for adapting LLMs to specialized domains and languages, potentially improving efficiency and effectiveness in medical applications.

RANK_REASON Academic paper on LLM adaptation strategies. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Continual Pretraining
• English
• French
• Hugging Face
• LLM-as-a-Judge
• MCqasim
• OEQA
• supervised fine-tuning

• paper
• model release

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