Mining experimental data from Materials Science literature with Large Language Models: an evaluation study

arXiv:2401.11052v2 Announce Type: replace
Abstract: This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we …

abstract arxiv capabilities cs.cl data documents evaluation experimental focus gpt gpt-3 gpt-3.5 gpt-3.5-turbo gpt-4 gpt-4-turbo information language language models large language large language models literature llms materials materials science mining science scientific study tasks turbo type