An Experiment with the Use of ChatGPT for LCSH Subject Assignment on Electronic Theses and Dissertations
This addresses cataloging efficiency for academic libraries, but is incremental as it relies on human verification.
The study explored using ChatGPT to generate Library of Congress Subject Headings (LCSH) for electronic theses and dissertations based on titles and abstracts, finding it could reduce cataloging time and improve resource discovery in academic libraries.
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.