Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. Numerous effective IFT datasets have been proposed in the recent past, but most focus on high resource languages such as English. In this work, we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual, Multi-turn instruction finetuning dataset, called M2Lingual, to better align LLMs on a diverse set of languages and tasks. M2Lingual contains a total of 182K IFT pairs that are built upon diverse seeds, covering 70 languages, 17 NLP tasks and general instruction-response pairs. LLMs finetuned with M2Lingual substantially outperform the majority of existing multilingual IFT datasets. Importantly, LLMs trained with M2Lingual consistently achieve competitive results across a wide variety of evaluation benchmarks compared to existing multilingual IFT datasets. Specifically, LLMs finetuned with M2Lingual achieve strong performance on our translated multilingual, multi-turn evaluation benchmark as well as a wide variety of multilingual tasks. Thus we contribute, and the 2 step Evol taxonomy used for its creation. M2Lingual repository - https://huggingface.co/datasets/ServiceNow-AI/M2Lingual

指导微调（IFT）对于使大型语言模型（LLM）遵循指令非常关键。本文提出了一个全新的完全合成的多语言多轮指导微调数据集（M2Lingual），称为Evol，以更好地使LLM在多种语言和任务中对齐。M2Lingual包含182K个基于不同种子构建的IFT对，涵盖了70种语言、17个NLP任务和一般的指令-响应对。使用M2Lingual微调的LLMs在许多现有的多语言IFT数据集中表现出色。重要的是，使用M2Lingual训练的LLMs在广泛的评估基准上始终能够达到与现有的多语言IFT数据集相媲美的竞争结果。因此，我们提出了用于创建M2Lingual的2步Evol分类法。

M2Lingual：大型语言模型中的多语言、多轮指令对齐增强