27 citations · 33 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 27 cited
Mining experimental data from Materials Science literature with Large Language Models: an evaluation study
Luca Foppiano, Guillaume Lambard, Toshiyuki Amagasa +1
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 scient…
cs.CL2023★ 6 cited
Semi-automatic staging area for high-quality structured data extraction from scientific literature
Luca Foppiano, Tomoya Mato, Kensei Terashima +7
We propose a semi-automatic staging area for efficiently building an accurate database of experimental physical properties of superconductors from literature, called SuperCon2, to…