MedInjection-FR: 探索原生、合成和翻译数据在生物医学指令微调中的作用
MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning
AI总结：
MedInjection-FR研究通过结合原生、合成和翻译数据，探索其在生物医学指令微调中的作用，发现原生数据效果最佳，混合数据提供互补优势，合成数据需与原生结合才能提升性能。
摘要：
指令微调已成为适应大型语言模型（LLMs）以遵循特定领域提示的关键步骤。然而，在医学等专业领域，高质量法语指令数据的稀缺限制了有效的监督。为了解决这一差距，我们介绍了MedInjection-FR，一个大规模的法语生物医学指令数据集，包含571,000对指令-响应数据，来源于三种互补的来源：原生、合成和翻译数据。我们设计了一个受控的实验框架，系统评估数据来源如何影响指令微调，使用在七种配置中微调的Qwen-4B-Instruct。结果显示，原生数据产生最强的性能，而混合设置，特别是原生和翻译数据，提供了互补的好处。合成数据单独使用效果较差，但与原生监督相结合时能产生积极影响。在开放性问答评估中结合了自动指标、LLM-as-a-judge评估和人类专家审查；尽管LLM基于判断与人类评分相关性最高，但它们对冗长性敏感。这些发现强调了数据真实性和多样性共同塑造下游适应，并且异质监督可以缓解原生法语医学指令的稀缺。
英文摘要：
Instruction tuning has become essential for adapting large language models (LLMs) to follow domain-specific prompts. Yet, in specialized fields such as medicine, the scarcity of high-quality French instruction data limits effective supervision. To address this gap, we introduce MedInjection-FR, a large-scale French biomedical instruction dataset comprising 571K instruction-response pairs drawn from three complementary sources: native, synthetic, and translated data. We design a controlled experimental framework to systematically assess how data provenance affects instruction tuning, using Qwen-4B-Instruct fine-tuned across seven configurations combining these sources. Results show that native data yield the strongest performance, while mixed setups, particularly native and translated, provide complementary benefits. Synthetic data alone remains less effective but contributes positively when balanced with native supervision. Evaluation on open-ended QA combines automatic metrics, LLM-as-a-judge assessment, and human expert review; although LLM-based judgments correlate best with human ratings, they show sensitivity to verbosity. These findings highlight that data authenticity and diversity jointly shape downstream adaptation and that heterogeneous supervision can mitigate the scarcity of native French medical instructions.
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