  Instruction finetuning (IFT) is critical for aligning Large Language Models
(LLMs) to follow instructions. While many effective IFT datasets have been
introduced recently, they predominantly focus on high-resource languages like
English. To better align LLMs across a broad spectrum of languages and tasks,
we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual,
Multi-turn instruction finetuning dataset, called M2Lingual. It is constructed
by first selecting a diverse set of seed examples and then utilizing the
proposed Evol taxonomy to convert these seeds into complex and challenging
multi-turn instructions. We demonstrate the effectiveness of M2Lingual by
training LLMs of varying sizes and showcasing the enhanced performance across a
diverse set of languages. We contribute the 2 step Evol taxonomy with the
guided generation code: https://github.com/ServiceNow/M2Lingual, as well as the
first fully synthetic, general and task-oriented, multi-turn, multilingual
dataset built with Evol - M2Lingual:
https://huggingface.co/datasets/ServiceNow-AI/ M2Lingual - containing 182K
total IFT pairs, covering 70 languages and 17+ NLP tasks.
