仅需少量多语言数据的多语言指令微调
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
AI总结：
本研究探究多语言大语言模型指令微调中的多语言数据作用，发现少量多语言样本即可显著提升跨语言指令遵循与泛化能力，为高效构建多语言指令微调模型提供新思路。
摘要：
随着经过指令微调的大语言模型（LLM）在全球范围内得到应用，它们遵循多语言指令的能力变得愈发关键。在本研究中，我们探究了多语言大语言模型指令微调过程中的多语言属性，如何影响其对预训练语料库中各语言的指令遵循能力。我们首先发现，即便仅采用单语言微调，许多语言也能将部分指令遵循能力迁移到其他语言。此外，我们发现在英文微调集中仅加入40个多语言样本，就能显著提升模型在微调过程中见过和未见过的语言上的多语言指令遵循能力。总体而言，我们观察到，尽管在这些语言上的训练样本数量少了10倍，采用多语言混合数据微调的模型在多种语言上的表现与单语言微调的模型相当甚至更优。最后，我们发现仅用2-4种语言丰富指令微调集，就能显著提升跨语言泛化能力。我们的研究结果表明，构建大规模多语言指令微调模型仅需非常少量的多语言指令-回复数据。
英文摘要：
As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial. In this work, we investigate how multilinguality during instruction tuning of a multilingual LLM affects instruction-following across languages from the pre-training corpus. We first show that many languages transfer some instruction-following capabilities to other languages from even monolingual tuning. Furthermore, we find that only 40 multilingual examples integrated in an English tuning set substantially improve multilingual instruction-following, both in seen and unseen languages during tuning. In general, we observe that models tuned on multilingual mixtures exhibit comparable or superior performance in multiple languages compared to monolingually tuned models, despite training on 10x fewer examples in those languages. Finally, we find that diversifying the instruction tuning set with even just 2-4 languages significantly improves cross-lingual generalization. Our results suggest that building massively multilingual instruction-tuned models can be done with only a very small set of multilingual instruction-responses.
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。
