# An Experiment with the Use of ChatGPT for LCSH Subject Assignment on Electronic Theses and Dissertations

arXiv: https://arxiv.org/abs/2403.16424 · HTML: https://mlsanity.com/paper/2403.16424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks](https://mlsanity.com/paper/2304.10145/) (2023)
- [Utilising a Large Language Model to Annotate Subject Metadata: A Case Study in an Australian National Research Data Catalogue](https://mlsanity.com/paper/2310.11318/) (2023)

## Built on by (most cited first, 3 of 3)

- [Web Archives Metadata Generation with GPT-4o: Challenges and Insights](https://mlsanity.com/paper/2411.05409/) (2024)
- [A Skill-Based AI Agentic Pipeline for Library of Congress Subject Indexing](https://mlsanity.com/paper/2605.03537/) (2026)
- [A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models](https://mlsanity.com/paper/2507.22913/) (2025)

## Abstract (arXiv)

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.