Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA

aarXivscore 0.31

Researchers studied domain adaptation strategies for medical LLMs in French, comparing continual pretraining, supervised fine-tuning, and their combination across multiple model sizes and families. The study used French medical question-answering as a testbed. Results show trade-offs between adaptation strategies, with no single best approach. You should consider these trade-offs when adapting LLMs to specialized domains and languages.

Key takeaways

Continual pretraining, supervised fine-tuning, and their combination were compared for medical LLM adaptation in French.
The study covered multiple model sizes and families, and three initialization types.
No single adaptation strategy outperformed the others across all scenarios.

#medical-llm #domain-adaptation #french-qa

Read the original

Feed

research17h ago

Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA

aarXiv

Key takeaways

Continual pretraining, supervised fine-tuning, and their combination were compared for medical LLM adaptation in French.
The study covered multiple model sizes and families, and three initialization types.
No single adaptation strategy outperformed the others across all scenarios.

#medical-llm #domain-adaptation #french-qa

Read at arXiv