This study delves into the potential use of large language models (LLMs) for generating Library of Congress Subject Headings (LCSH). The authors employed ChatGPT to generate subject headings for electronic theses and dissertations (ETDs) based on their titles and abstracts. The results suggests that LLMs such as ChatGPT have the potential to reduce cataloging time needed for assigning LCSH subject terms for ETDs as well as to improve the discovery of this type of resource in academic libraries. Nonetheless, human catalogers remain essential for verifying and enhancing the validity, exhaustivity, and specificity of LCSH generated by LLMs.

翻译：本研究深入探讨了利用大型语言模型生成国会图书馆主题词的潜在应用。作者基于电子学位论文的标题和摘要，采用ChatGPT生成相应的主题词。结果表明，ChatGPT等大型语言模型有望减少为电子学位论文分配国会图书馆主题词所需的编目时间，并提升此类资源在学术图书馆中的可发现性。然而，人工编目员在验证与提升LLM生成主题词的有效性、完备性与专指性方面仍然不可或缺。