M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models
This addresses the problem of aligning LLMs to instructions in low-resource languages for multilingual NLP applications, though it is incremental as it builds on existing IFT methods.
The paper tackles the lack of multilingual instruction finetuning datasets by introducing M2Lingual, a fully synthetic dataset covering 70 languages and 17+ NLP tasks, which enhances LLM performance across diverse languages when used for training.
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.