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。

Aya 数据集：一份用于多语言指导调优的开放获取集合

Aya Dataset: An Open-Access Collection for Multilingual Instruction  Tuning

HCI researchers have been gradually shifting attention from individual users
to communities when engaging in research, design, and system development.
However, our field has yet to establish a cohesive, systematic understanding of
the challenges, benefits, and commitments of community-collaborative approaches
to research. We conducted a systematic review and thematic analysis of 47
computing research papers discussing participatory research with communities
for the development of technological artifacts and systems, published over the
last two decades. From this review, we identified seven themes associated with
the evolution of a project: from establishing community partnerships to
sustaining results. Our findings suggest that several tensions characterize
these projects, many of which relate to the power and position of researchers,
and the computing research environment, relative to community partners. We
discuss the implications of our findings and offer methodological proposals to
guide HCI, and computing research more broadly, towards practices that center
communities.

本文使用系统性回顾和主题分析，总结了过去 20 年中与社区合作进行参与式研究的计算研究论文，发现从建立社区伙伴关系到维持成果的演变涉及多种挑战与紧张关系，其解决方法包括制定关于社区社会地位和研究者地位的策略，建议人 - 机交互领域更加注重社区对计算研究的贡献和参与。

计算机研究中社区协作方法的系统综述和主题分析

A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research

Research in NLP lacks geographic diversity, and the question of how NLP can
be scaled to low-resourced languages has not yet been adequately solved.
"Low-resourced"-ness is a complex problem going beyond data availability and
reflects systemic problems in society. In this paper, we focus on the task of
Machine Translation (MT), that plays a crucial role for information
accessibility and communication worldwide. Despite immense improvements in MT
over the past decade, MT is centered around a few high-resourced languages. As
MT researchers cannot solve the problem of low-resourcedness alone, we propose
participatory research as a means to involve all necessary agents required in
the MT development process. We demonstrate the feasibility and scalability of
participatory research with a case study on MT for African languages. Its
implementation leads to a collection of novel translation datasets, MT
benchmarks for over 30 languages, with human evaluations for a third of them,
and enables participants without formal training to make a unique scientific
contribution. Benchmarks, models, data, code, and evaluation results are
released under this https URL

本文探讨了如何利用参与性研究，将机器翻译 (MT) 技术应用于非洲语言，以解决 NLP 在地理分布上欠缺多样性并实现 NLP 的规模化问题。我们提出了一种可行性高并可扩展的情境下的参与式研究方案，其使非专业人员也能对 MT 技术的开发做出独特的科学贡献，并制定了适用于 30 多种语言的 MT 基准。