Error Analysis in ChatGPT’s MARC21 Records: A Study of RDA Conformity

Authors

  • Department of Studies in Library and Information Science, University of Mysore, Mysuru – 570005, Karnataka
  • Department of Studies in Library and Information Science, University of Mysore, Mysuru – 570005, Karnataka
  • Department of Studies in Library and Information Science, University of Mysore, Mysuru – 570005, Karnataka

DOI:

https://doi.org/10.17821/srels/2024/v61i4/171481

Keywords:

Artificial Intelligence, Cataloguing, ChatGPT, MARC21, Resource Description and Access, Quality of Cataloguing

Abstract

This study aims to evaluate the accuracy and quality of MARC21 catalogue records generated by ChatGPT using bibliographic data from title pages. The study will shed light on the effectiveness and reliability of automated cataloguing processes utilising AI technology. This involves examining factors such as correctness, consistency, and adherence to Resource Description and Access (RDA) standards. The analysis highlights variations in error rates across different records. Identifying the underlying causes of errors in records with higher rates can help in implementing targeted improvements to enhance the data quality and consistency of ChatGPT-generated catalogue records.

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References

American Library Association. (2014). Resource Description and Access. American Library Association.

Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. College of Business Faculty Publications. https://digitalcommons.uri.edu/cba_facpubs/548

Bodenhamer, J. (2023). The reliability and usability of ChatGPT for library metadata. ShareOk. https://shareok.org/handle/11244/339626

ChatGPT. (2024). ChatGPT, How well are you trained in MARC21 and RDA? What are your capabilities in understanding RDA records in MARC21 format? OpenAI platform.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., …, Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26-29. https://doi.org/10.1108/LHTN-01-2023-0009

Taecharungroj, V. (2023). What Can ChatGPT Do? Analyzing early reactions to the innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35. https://doi.org/10.3390/bdcc7010035

Published

2024-09-11

How to Cite

Niveditha, B., Harinarayana, N. S., & Balachandran, C. (2024). Error Analysis in ChatGPT’s MARC21 Records: A Study of RDA Conformity. Journal of Information and Knowledge, 61(4), 187–195. https://doi.org/10.17821/srels/2024/v61i4/171481

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