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 summaries. The results revealed that although some generated subject headings were valid, there were issues regarding specificity and exhaustiveness. The study showcases that LLMs can serve as a strategic response to the backlog of items awaiting cataloging in academic libraries, while also offering a cost-effective approach for promptly generating LCSH. Nonetheless, human catalogers remain essential for verifying and enhancing the validity, exhaustiveness, and specificity of LCSH generated by LLMs.

中文翻译：

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使用 ChatGPT 进行电子学位论文 LCSH 科目分配的实验

这项研究深入探讨了大型语言模型 (LLM) 用于生成国会图书馆主题词 (LCSH) 的潜在用途。作者使用 ChatGPT 根据标题和摘要生成电子论文 (ETD) 的主题词。结果显示，虽然一些生成的主题标题是有效的，但存在具体性和详尽性方面的问题。该研究表明，法学硕士可以作为对学术图书馆等待编目积压的文献的战略回应，同时还提供一种具有成本效益的方法来迅速生成 LCSH。尽管如此，人类编目员对于验证和增强法学硕士生成的 LCSH 的有效性、详尽性和特异性仍然至关重要。