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2023 IEEE International Conference on Big Data (BigData)

When LLM Meets Material Science: An Investigation on MOF Synthesis Labeling

Year: 2023, Pages: 6320-6321

Authors

Xintong Zhao, Drexel University,Metadata Research Center,Philadelphia,USA  
Kyle Langlois, University of Central Florida,Department of Chemistry,Orlando,USA  
Jacob Furst, University of Central Florida,Department of Chemistry,Orlando,USA  
Scott McClellan, Drexel University,Metadata Research Center,Philadelphia,USA  
Rob Fleur, Drexel University,Metadata Research Center,Philadelphia,USA  
Yuan An, Drexel University,Metadata Research Center,Philadelphia,USA  
Xiaohua Hu, Drexel University,Metadata Research Center,Philadelphia,USA  
Fernando Uribe-Romo, University of Central Florida,Department of Chemistry,Orlando,USA  
Diego Gualdron, Chemical and Biological Engineering, Colorado School of Mines,Golden,USA  
Jane Greenberg, Drexel University,Metadata Research Center,Philadelphia,USA
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Abstract

Recent developments in Large Language Models (LLMs) have advanced the natural language processing (NLP) studies to a new era [1], [2], [4]–[6]. In generic domains, LLMs have become a key component in wide variety of state-of-the-art NLP tasks. In addition, prompt learning enables LLMs-based models to reach robust performance with much smaller training data.
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