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The rapid evolution of Large Language Models (LLMs) underscores the criticalimportance 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 ethicaldata stewardship, their application in LLM training data is less prevalent, anissue our research aims to address. Our study begins with a review of existingliterature, highlighting the significance of FAIR principles in data managementfor model training. Building on this foundation, we introduce a novel frameworkthat incorporates FAIR principles into the LLM training process. A key aspectof this approach is a comprehensive checklist, designed to assist researchersand developers in consistently applying FAIR data principles throughout themodel development lifecycle. The practicality and effectiveness of ourframework are demonstrated through a case study that involves creating aFAIR-compliant dataset to detect and reduce biases. This case study not onlyvalidates the usefulness of our framework but also establishes new benchmarksfor more equitable, transparent, and ethical practices in LLM training. Weoffer this framework to the community as a means to promote technologicallyadvanced, ethically sound, and socially responsible AI models.

FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?