ReSURE reduces the harm of noisy dialogue data during fine-tuning by grouping samples by dialogue depth and softly down-weighting high-loss examples, improving multi-turn benchmarks modestly.
M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in Large Language Models
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abstract
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.
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2025 1verdicts
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ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning
ReSURE reduces the harm of noisy dialogue data during fine-tuning by grouping samples by dialogue depth and softly down-weighting high-loss examples, improving multi-turn benchmarks modestly.