持续预训练能否缩小通用与专用语言模型在医疗领域的性能差距？
Can Continual Pre-training Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain?
AI总结：
本文通过持续预训练和融合技术，缩小专用模型与大通用模型的性能差距，构建了DeFineMed模型家族，证明专用模型在医疗任务中具有竞争力。
摘要：
本文通过持续预训练和融合技术，缩小专用模型与大通用模型的性能差距，构建了DeFineMed模型家族，证明专用模型在医疗任务中具有竞争力。
英文摘要：
This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via continual pre-training and merging. We address the scarcity of specialized non-English data by constructing a high-quality German medical corpus (FineMed-de) from FineWeb2. This corpus is used to continually pre-train and merge three well-known LLMs (ranging from $7B$ to $24B$ parameters), creating the DeFineMed model family. A comprehensive evaluation confirms that specialization dramatically enhances $7B$ model performance on German medical benchmarks. Furthermore, the pairwise win-rate analysis of the Qwen2.5-based models demonstrates an approximately $3.5$-fold increase in the win-rate against the much larger Mistral-Small-24B-Instruct through domain adaptation. This evidence positions specialized $7B$ models as a competitive, resource-efficient solution for complex medical instruction-following tasks. While model merging successfully restores instruction-following abilities, a subsequent failure mode analysis reveals inherent trade-offs, including the introduction of language mixing and increased verbosity, highlighting the need for more targeted fine-tuning in future work. This research provides a robust, compliant methodology for developing specialized LLMs, serving as the foundation for practical use in German-speaking healthcare contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
