We are excited to share two new papers showing great capabilities for large language models to extract data from papers. This work originated as a Skunkworks project. Congratulations to undergraduates Shrey Modi, Anna Latosinska, Jinming Zhang, Ching-Wen Wang, Shanonan Wang, and Ayan Deep Hazra for their contributions and coauthorship on the first paper.
News piece from UW CoE: https://lnkd.in/g7n32M8n
Paper 1 – Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models – https://lnkd.in/g6yx4zCH
Paper 2 – Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering — Example of ChatGPT – https://lnkd.in/gTPwbqXv