M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models
<|reference_start|>M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models: 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.<|reference_end|>
@article{maheshwary2024m2lingual:,
    title={M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in
  Large Language Models},
    author={Rishabh Maheshwary and Vikas Yadav and Hoang Nguyen and Khyati Mahajan
  and Sathwik Tejaswi Madhusudhan},
    journal={arXiv preprint arXiv:2406.16783},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16783},
    primaryClass={cs.CL cs.AI cs.LG}
}