Luth让小模型高效专精法语，同时保持英语能力。
Luth: Efficient French Specialization for Small Language Models and Cross-Lingual Transfer
- 用高质量法语文本针对性微调，提升法语表现
- 在多个法语基准上超越同规模开源模型
- 适合需要双语能力的法语研究与应用
大型语言模型仍以英语为主，导致其他主要语言如法语在小型语言模型（SLMs）中表现显著落后。现有多语言模型在法语上的性能远低于英语，且针对法语的高效适配方法研究有限。为此，我们提出Luth系列法语专精的小型语言模型：通过在精选的高质量法语文本上进行定向后训练，我们的模型在多个法语基准测试中超越所有同规模开源模型，同时保留原有英语能力。进一步发现，策略性模型融合可提升双语性能，确立Luth为法语小型语言模型的新基准，为未来法语研究提供可靠基线。
原文摘要 · Abstract (English)
The landscape of Large Language Models remains predominantly English-centric, resulting in a significant performance gap for other major languages, such as French, especially in the context of Small Language Models (SLMs). Existing multilingual models demonstrate considerably lower performance in French compared to English, and research on efficient adaptation methods for French remains limited. To address this, we introduce \textbf{Luth}, a family of French-specialized SLMs: through targeted post-training on curated, high-quality French data, our models outperform all open-source counterparts of comparable size on multiple French benchmarks while retaining their original English capabilities. We further show that strategic model merging enhances performance in both languages, establishing Luth as a new state of the art for French SLMs and a robust baseline for future French-language research.
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