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

arXiv:2401.11033v3 Announce Type: replace
Abstract: 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 …

abstract ai development arxiv cs.cl data data integrity dataset development ethical ethical considerations evolution fair importance integrity language language models large language large language models llms role training type