arXiv:2606.19266v1 Announce Type: cross Abstract: The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized domains and languages, yet the effectiveness of domain adaptation strategies remains unclear. We present a study of medical domain adaptation using French medical question-answering (QA) as a case study. We compare continual pretraining (CPT), supervised fine-tuning (SFT), and their combination across three model families, multiple sizes, and three initialization types, explicitly disentangling adaptation effects from base model choi
The rapid advancement and deployment of LLMs necessitate a deeper understanding of their real-world applicability and adaptation strategies across specialized domains and languages.
This study provides empirical evidence on effective methods for LLM adaptation in a critical, regulated domain like medicine and a specific language, offering insights into optimizing performance for practical applications.
We now have a clearer empirical basis for comparing different LLM adaptation strategies (CPT, SFT) and their effectiveness across various model architectures and initializations in specialized, non-English contexts.
- · AI developers
- · Healthcare sector
- · Non-English speaking markets
- · Academic researchers
- · Generic LLM providers
- · Translators without AI tools
Improved performance and reliability of medical LLMs in French, leading to better diagnostic support and information access.
Increased adoption of specialized LLMs in other regulated, multilingual domains, driving further investment in adaptation research.
The emergence of 'domain-specific AI specialists' that outperform generalist models, leading to niche AI market fragmentation and new regulatory challenges.
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Read at arXiv cs.AI