Datasets are foundational to many breakthroughs in modern artificial
intelligence. Many recent achievements in the space of natural language
processing (NLP) can be attributed to the finetuning of pre-trained models on a
diverse set of tasks that enables a large language model (LLM) to respond to
instructions. Instruction fine-tuning (IFT) requires specifically constructed
and annotated datasets. However, existing datasets are almost all in the
English language. In this work, our primary goal is to bridge the language gap
by building a human-curated instruction-following dataset spanning 65
languages. We worked with fluent speakers of languages from around the world to
collect natural instances of instructions and completions. Furthermore, we
create the most extensive multilingual collection to date, comprising 513
million instances through templating and translating existing datasets across
114 languages. In total, we contribute four key resources: we develop and
open-source the Aya Annotation Platform, the Aya Dataset, the Aya Collection,
and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case
study in participatory research, involving collaborators from 119 countries. We
see this as a valuable framework for future research collaborations that aim to
bridge gaps in resources.

本研究针对英语语言数据集的局限性，通过与全球流利的语言使用者合作，建立了覆盖 65 种语言的人工策划指令跟随数据集，并通过模板和翻译现有数据集创建了迄今最广泛的多语言数据集，共包含 5.13 亿个实例。此外，我们还提供 Aya 注释平台、Aya 数据集、Aya 集合和 Aya 评估套件等四个关键资源，而且 Aya 倡议还是一项有价值的参与性研究案例，涉及来自 119 个国家的合作者，我们认为此为未来旨在弥补资源差距的研究合作提供了宝贵的框架。