Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering -- Example of ChatGPT

There has been a growing effort to replace hand extraction of data from research papers with automated data extraction based on natural language processing (NLP), language models (LMs), 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 essentially no initial effort or background using an advanced conversational LLM (or AI). ChatExtract consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract data, and assure its correctness through a series of follow-up questions. These follow-up questions address a critical challenge associated with LLMs - their tendency to provide factually inaccurate responses. ChatExtract can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data we find precision and recall both over 90% from the best conversational LLMs, likely rivaling or exceeding human accuracy in many cases. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to ChatExtract, due to their simplicity, transferability and accuracy are likely to replace other methods of data extraction in the near future.

Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models

Accurate and comprehensive material databases extracted from research papers are critical for materials science and engineering but require significant human effort to develop. In this paper we present a simple method of extracting materials data from full texts of research papers suitable for quickly developing modest-sized databases. The method requires minimal to no coding, prior knowledge about the extracted property, or model training, and provides high recall and almost perfect precision in the resultant database. The method is fully automated except for one human-assisted step, which typically requires just a few hours of human labor. The method builds on top of natural language processing and large general language models but can work with almost any such model. The language models GPT-3/3.5, bart and DeBERTaV3 are evaluated here for comparison. We provide a detailed detailed analysis of the methods performance in extracting bulk modulus data, obtaining up to 90% precision at 96% recall, depending on the amount of human effort involved. We then demonstrate the methods broader effectiveness by developing a database of critical cooling rates for metallic glasses.