Summary of An Experiment with the Use Of Chatgpt For Lcsh Subject Assignment on Electronic Theses and Dissertations, by Eric H. C. Chow et al.
An Experiment with the Use of ChatGPT for LCSH Subject Assignment on Electronic Theses and Dissertations
by Eric H. C. Chow, TJ Kao, Xiaoli Li
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Digital Libraries (cs.DL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of large language models (LLMs) like ChatGPT to generate Library of Congress Subject Headings (LCSH) for electronic theses and dissertations (ETDs). The authors use ChatGPT to produce subject headings based on titles and abstracts, finding that LLMs can potentially reduce cataloging time and improve discovery in academic libraries. While LLMs show promise, human catalogers remain crucial for verifying and enhancing the accuracy, exhaustivity, and specificity of generated LCSH. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses special computers called large language models to help create labels for electronic papers. These labels are important because they help people find these papers in big libraries. The computer was pretty good at making labels, which could save time and make it easier to find the papers. But human helpers are still needed to check the labels and make sure they’re perfect. |
