Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models

arXiv:2404.05893v1 Announce Type: cross
Abstract: 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 …

abstract accessibility arxiv cs.ai cs.cl cs.ir curation data datasets findability gpt gpt-4 interoperability knowledge knowledge base language language models large language large language models llms metadata paper random records role standards type