Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering

arXiv:2303.05352v3 Announce Type: replace
Abstract: There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work we propose the ChatExtract method that can fully automate very accurate data extraction with minimal …

abstract arxiv automated cond-mat.mtrl-sci conversational cs.cl data data extraction engineering extraction language language models language processing large language large language models llms materials natural natural language natural language processing papers processing prompt research research papers type