Mining experimental data from materials science literature with large language models: an evaluation study

Mining experimental data from materials science literature with large language models: an evaluation study

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 scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i...

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Bibliographic Details
Main Authors: Luca Foppiano, Guillaume Lambard, Toshiyuki Amagasa, Masashi Ishii
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Science and Technology of Advanced Materials: Methods
Subjects:
Large language models
benchmark
NER
TDM
evaluation
materials science
Online Access:https://www.tandfonline.com/doi/10.1080/27660400.2024.2356506

Internet

https://www.tandfonline.com/doi/10.1080/27660400.2024.2356506

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