  Metadata play a crucial role in ensuring the findability, accessibility,
interoperability, and reusability of datasets. This paper investigates the
potential of large language models (LLMs), specifically GPT-4, to improve
adherence to metadata standards. We conducted experiments on 200 random data
records describing human samples relating to lung cancer from the NCBI
BioSample repository, evaluating GPT-4's ability to suggest edits for adherence
to metadata standards. We computed the adherence accuracy of field name-field
value pairs through a peer review process, and we observed a marginal average
improvement in adherence to the standard data dictionary from 79% to 80%
(p<0.5). We then prompted GPT-4 with domain information in the form of the
textual descriptions of CEDAR templates and recorded a significant improvement
to 97% from 79% (p<0.01). These results indicate that, while LLMs may not be
able to correct legacy metadata to ensure satisfactory adherence to standards
when unaided, they do show promise for use in automated metadata curation when
integrated with a structured knowledge base
