MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
This addresses the problem of data scarcity for low-resource language AI development, offering a scalable solution for researchers and practitioners in multilingual NLP.
The paper tackles the challenge of creating instruction tuning datasets for low-resource languages without human annotation by introducing MURI, a method that generates over 2 million instruction-output pairs across 200 languages, showing effectiveness in NLU and generation tasks.
Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets face serious challenges for low-resource languages due to their dependence on data annotation. This work introduces a novel method, Multilingual Reverse Instructions (MURI), which generates high-quality instruction tuning datasets for low-resource languages without requiring human annotators or pre-existing multilingual models. Utilizing reverse instructions and a translation pipeline, MURI produces instruction-output pairs from existing human-written texts in low-resource languages. This method ensures cultural relevance and diversity by sourcing texts from different native domains and applying filters to eliminate inappropriate content. Our dataset, MURI-IT, includes more than 2 million instruction-output pairs across 200 languages. Evaluation by native speakers and fine-tuning experiments with mT5 models demonstrate the approach's effectiveness for both NLU and open-ended generation. We publicly release datasets and models at https://github.com/akoksal/muri.