Extracting accurate materials data from research papers with conversational language models and prompt engineering

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 initial effort and background, using an advanced conversational LLM. ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data,…

• NS
  National Science Foundation

  Awards: 2137603, 1931298, 2138296, 2138307, 2138286
• OO
  Office of Advanced Cyberinfrastructure

  Awards: 2138286, 2138259, 1931298, 2137603, 2138307, 2138296