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.

翻译：本研究探讨了利用大型语言模型（LLMs）生成《国会图书馆主题标目》（LCSH）的潜在应用。作者使用ChatGPT，依据电子学位论文（ETDs）的标题和摘要为其生成主题标目。结果表明，尽管部分生成的主题标目有效，但在专指度和详尽性方面仍存在问题。该研究揭示，LLMs可作为应对学术图书馆待编目文献积压问题的策略性工具，同时为即时生成LCSH提供了一种经济高效的方法。尽管如此，人工编目员对于验证和提升LLMs生成的LCSH的有效性、详尽性和专指度仍不可或缺。