Assessing the performance of generative artificial intelligence in retrieving information against manually curated genetic and genomic data

Curated resources at centralized repositories provide high-value service to users by enhancing data veracity. Curation, however, comes with a cost, as it requires dedicated time and effort from personnel with deep domain knowledge. In this paper, we investigate the performance of a large language model (LLM), specifically generative pre-trained transformer (GPT)-3.5 and GPT-4, in extracting and presenting data against a human curator. In order to accomplish this task, we used a small set of...