英语并非一切所需：系统探索多语言在大语言模型后训练中的作用
English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
AI总结：
研究通过220次监督微调实验，探讨训练语言覆盖、模型规模和任务领域间的相互作用，发现增加后训练语言覆盖对各任务和模型规模均有益，低资源语言受益最大，高资源语言趋于稳定。单语种多语言性可提升英语表现和跨语言泛化，英语独占后训练效果不佳。
摘要：
尽管大型语言模型广泛部署于多种语言，后训练流程仍以英语为中心，导致语言间性能差异。本文系统研究了训练语言覆盖、模型规模和任务领域之间的相互作用，基于220次监督微调运行，在涵盖数学推理和API调用任务的平行翻译多语言数据混合上进行测试，模型参数高达8B。研究发现，增加后训练语言覆盖对各任务和模型规模均有益，低资源语言受益最大，高资源语言趋于稳定。即使少量多语言性也有帮助：加入一种非英语语言可提升英语表现和跨语言泛化能力，使英语独占后训练效果不佳。此外，在足够语言多样性下，零样本跨语言迁移可匹配或超越低多样性设置中直接语言包含的效果，尽管对于构型遥远、低资源语言，增益仍有限。
英文摘要：
Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities across languages. We present a systematic, controlled study of the interplay between training language coverage, model scale, and task domain, based on 220 supervised fine-tuning runs on parallel translated multilingual data mixtures spanning mathematical reasoning and API calling tasks, with models up to 8B parameters. We find that English-only post-training is typically suboptimal: incorporating even a single non-English language improves both English performance and cross-lingual generalization. Increasing language diversity during post-training generally yields further gains, particularly for low-resource languages, while performance on high-resource languages tends to plateau rather than degrade. Moreover, greater language diversity enables strong zero-shot transfer to unseen languages, reducing the need for direct inclusion, though gains remain limited for typologically distant, low-resource languages.
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。
