介绍(产科)
计算机科学
自然语言处理
数据科学
情报检索
人工智能
医学
放射科
作者
Hasan M. Sayeed,Wade Smallwood,Sterling G. Baird,Taylor D. Sparks
出处
期刊:Matter
[Elsevier]
日期:2024-03-01
卷期号:7 (3): 723-727
被引量:2
标识
DOI:10.1016/j.matt.2023.12.032
摘要
Large language models (LLMs) revolutionized how we engage with information. In materials science, we aim to leverage natural language processing to transform progress and discovery. Analyzing diverse materials science papers, we annotate data types and sources, laying the groundwork for targeted information extraction and LLM development. Large language models (LLMs) revolutionized how we engage with information. In materials science, we aim to leverage natural language processing to transform progress and discovery. Analyzing diverse materials science papers, we annotate data types and sources, laying the groundwork for targeted information extraction and LLM development.
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