Leveraging Large Language Models for Processing and Evaluating FAIR Digital Objects

This paper explores the potential of generative AI, i.e., Large Language Models, for processing FAIR Digital Objects and enhancing their reusability assessment. By leveraging ChatGPT’s o3-mini model, the authors propose a lightweight workflow of prompt-based operations that automates the resolution of persistent Handle identifiers, extracts the corresponding metadata into structured key-value pairs, and evaluates the content. For demonstration, we experimented with a detailed case study. We processed FAIR Digital Objects from different domains using a series of targeted prompts, requesting ChatGPT to interpret the syntactic and semantic features of the corresponding metadata elements. Through a set of competency questions, the preliminary results reveal that while ChatGPT successfully indexes and analyzes most of the metadata content, challenges remain in the semantic interpretation of record elements, fully automated resolving of non-URL based references, and targeted prompting for optimizing the information content of the outputs. The findings underscore the potential of generative AI-assisted methods to reduce manual effort, improve data reuse, and ultimately foster data infrastructures in the spirit of FAIR Principles.