MURI: High-Quality Instruction Tuning Datasets for Low-Resource
  Languages via Reverse Instructions
Authors: name: Abdullatif Köksal, name: Marion Thaler, name: Ayyoob Imani, name: Ahmet Üstün, name: Anna Korhonen, name: Hinrich Schütze

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.