Using ChatGPT for LCSH Subject Assignment on Electronic Theses and Dissertations

Core Concepts

Large Language Models like ChatGPT can assist in generating Library of Congress Subject Headings for electronic theses and dissertations, but human catalogers are still essential for ensuring validity, exhaustiveness, and specificity.

Abstract

Abstract:

Experiment using ChatGPT for LCSH assignment.
Validity issues with generated subject headings.
Importance of human catalogers in verifying LCSH.
Introduction:

Overview of MARC and LCSH systems.
Evolution and importance of LCSH.
Automatic Cataloging Record Generation:

Previous studies using machine learning for subject heading assignment.
The role of LLMs like ChatGPT in automating tasks.
Subject Analysis for Theses and Dissertations:

Challenges in subject analysis for ETDs.
Use of author-supplied keywords vs. controlled vocabularies.
Methodology:

Description of the prompt used with ChatGPT.
Results:

Evaluation of MARC coding correctness by ChatGPT.
Validity issues with assigned LCSH terms.
Discussion:

Solutions to address validity issues in LCSH assignment by ChatGPT.
Conclusion:

Potential benefits and limitations of using LLMs like ChatGPT in library cataloging.

Stats

The cost associated with using Microsoft’s OpenAI API amounted to approximately USD $0.25.

Quotes

"LLMs can serve as a strategic response to the backlog of items awaiting cataloging."
"Human catalogers remain essential for verifying the validity, exhaustiveness, and specificity of LCSH generated by LLMs."

Key Insights Distilled From

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

by Eric H. C. C... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16424.pdf

Deeper Inquiries

How can libraries ensure consistency when utilizing both LLMs like ChatGPT and human catalogers?

In order to ensure consistency when using both Large Language Models (LLMs) such as ChatGPT and human catalogers, libraries can implement several strategies:

Establish Clear Guidelines: Libraries should develop clear guidelines and protocols for the use of LLMs in conjunction with human catalogers. These guidelines should outline the specific roles and responsibilities of each party in the cataloging process.

Training and Collaboration: Provide training to human catalogers on how to work effectively with LLM-generated data. Encourage collaboration between LLMs and human experts to cross-validate results and address any discrepancies.

Quality Control Mechanisms: Implement quality control mechanisms such as regular audits, spot-checking, or peer reviews to verify the accuracy of subject headings generated by LLMs against established standards.

Feedback Loops: Establish feedback loops where human catalogers can provide input on the performance of LLM-generated subject headings, helping to refine and improve future outputs.

Continuous Monitoring: Continuously monitor the performance of LLMs over time, adjusting workflows or processes as needed based on feedback from human catalogers.

By implementing these strategies, libraries can maintain consistency in their cataloging processes while leveraging the efficiency gains offered by LLM technology.

What ethical considerations should be taken into account when automating tasks traditionally done by humans?

When automating tasks traditionally performed by humans using technologies like Large Language Models (LLMs), several ethical considerations must be taken into account:

Bias Mitigation: Ensure that automated systems are designed to mitigate biases present in training data that could perpetuate discrimination or inequity in decision-making processes.

Transparency: Maintain transparency about the use of automation technologies within library operations, including disclosing when AI tools are used for tasks like subject heading assignment.

Accountability: Clarify accountability structures for decisions made by automated systems, ensuring there are mechanisms in place to address errors or unintended consequences.

Data Privacy: Safeguard user privacy rights by protecting sensitive information contained within library records from unauthorized access or misuse during automation processes.

Human Oversight: Retain a level of human oversight throughout automated processes to intervene in cases where ethical concerns arise or where critical judgment is required beyond what AI systems can provide autonomously.

How might advancements in LLM technology impact the future role of human catalogers?

Advancements in Large Language Model (LLM) technology have significant implications for the future role of human catalogers:

Efficiency Gains: As LLMs become more sophisticated at generating metadata like Library of Congress Subject Headings (LCSH), they can streamline routine tasks previously handled manually by reducing processing time and increasing productivity among cataloging staff.

2 .Focus on Value-Added Tasks: Human catalogers may shift their focus towards higher-value activities such as quality assurance, metadata enrichment, user engagement initiatives rather than spending extensive time on repetitive classification tasks.
3 .Skill Enhancement: Catalogers may need to upskill themselves with knowledge related specifically around working alongside AI tools efficiently understanding output nuances improving overall workflow effectiveness.
4 .Collaborative Workflows: Future workflows may involve closer collaboration between AI systems like ChatGPT & expert librarians who will work together synergistically combining machine efficiency with nuanced expertise provided only through experience & domain knowledge.
5 .Adaptation & Learning: Human Cataloguers will need adaptability skills learning continuously evolving tech landscape staying updated new developments ensuring seamless integration advanced tech solutions into traditional library practices maintaining relevance & efficiency.