FAIR Enough:Develop and Assess a FAIR-Compliant Dataset for Large Language Model Training?

The rapid evolution of Large Language Models(LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR(Findable, Accessible, Interoperable, Reusable) data principles. While these principles are crucial for ethical data stewardship, their specific application in the context of LLM training data remains an under-explored area. This research gap is the focus of our study, which begins with an examination of existing literature to underline the importance of FAIR principles in managing data for LLM training. Building upon this, we propose a novel frame-work designed to integrate FAIR principles into the LLM development lifecycle. A contribution of our work is the development of a comprehensive checklist intended to guide researchers and developers in applying FAIR data principles consistently across the model development process. The utility and effectiveness of our frame-work are validated through a case study on creating a FAIR-compliant dataset aimed at detecting and mitigating biases in LLMs. We present this framework to the community as a tool to foster the creation of technologically advanced, ethically grounded, and socially responsible AI models.

A Semantic Approach to Workflow Management and Reuse for Research Problem Solving

The investigation proposes the application of an ontological semantic approach to describing workflow control patterns,research workflow step patterns,and the meaning of the workflows in terms of domain knowledge.The approach can provide wide opportunities for semantic refinement,reuse,and composition of workflows.Automatic reasoning allows verifying those compositions and implementations and provides machine-actionable workflow manipulation and problem-solving using workflows.The described approach can take into account the implementation of workflows in different workflow management systems,the organization of workflows collections in data infrastructures and the search for them,the semantic approach to the selection of workflows and resources in the research domain,the creation of research step patterns and their implementation reusing fragments of existing workflows,the possibility of automation of problemsolving based on the reuse of workflows.The application of the approach to CWFR conceptions is proposed.