The rapid evolution of Large Language Models (LLMs) underscores the critical importance of ethical considerations and data integrity in AI development, emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles. While these principles have long been a cornerstone of ethical data stewardship, their application in LLM training data is less prevalent, an issue our research aims to address. Our study begins with a review of existing literature, highlighting the significance of FAIR principles in data management for model training. Building on this foundation, we introduce a novel framework that incorporates FAIR principles into the LLM training process. A key aspect of this approach is a comprehensive checklist, designed to assist researchers and developers in consistently applying FAIR data principles throughout the model development lifecycle. The practicality and effectiveness of our framework are demonstrated through a case study that involves creating a FAIR-compliant dataset to detect and reduce biases. This case study not only validates the usefulness of our framework but also establishes new benchmarks for more equitable, transparent, and ethical practices in LLM training. We offer this framework to the community as a means to promote technologically advanced, ethically sound, and socially responsible AI models.

翻译：大型语言模型（LLMs）的快速发展凸显了人工智能开发中伦理考量和数据完整性的关键重要性，并强调了FAIR（可发现、可访问、可互操作、可复用）数据原则的作用。尽管这些原则长期以来一直是伦理数据管理的基石，但它们在LLM训练数据中的应用尚未普及，本研究旨在解决这一问题。我们首先回顾现有文献，强调FAIR原则在模型训练数据管理中的重要性。在此基础上，我们提出一个将FAIR原则融入LLM训练流程的新框架。该框架的关键要素是一份综合性检查清单，旨在帮助研究人员和开发者在模型开发生命周期中持续应用FAIR数据原则。通过一个创建符合FAIR原则的数据集以检测和减少偏见的案例研究，我们展示了该框架的实用性和有效性。该案例研究不仅验证了框架的实用性，还为LLM训练中更公平、透明、伦理的实践确立了新基准。我们将此框架提供给学术界，以推动技术先进、伦理稳健且对社会负责的人工智能模型发展。