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

Xintong Zhao, Kyle Langlois, Jacob Furst,Scott McClellan, Rob Fleur,Yuan An,Xiaohua Hu ,Fernando J. Uribe-Romo, Diego A. Gómez-Gualdrón,Jane Greenberg

2023 IEEE International Conference on Big Data (BigData)（2023）

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摘要

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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关键词

Metal–organic Frameworks,Large Language Models,Domain Generalization,Natural Language Processing Tasks,Small Training Data,Entity Types,Named Entity Recognition,Digital Object Identifier

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