The Feasibility Study on Using ChatGPT to Generate MARC21 Bibliographic Records

This study aims to investigate the feasibility of using ChatGPT to generate MARC21 bibliographic records in terms of accuracy, error, addition and missing rate. A total of 55 cataloging in publication records from the bibliographic database of “National Bibliographic Information Network” (NBINet) provided by National Central Library in Taiwan were selected as the study sample and were employed as part of prompt to request ChatGPT-4o to generate MARC21 records. Records from the NBINet have served as the benchmark for evaluation of MARC21 records generated by ChatGPT-4o. The findings indicate that ChatGPT-4o achieved an accuracy rate of 98.4%, an error rate of 1.6%, an addition rate of 102%, and a missing rate of 27%. In terms of similarity, the average similarity between the ChatGPT-generated records and the preliminary cataloging data was found to be 3.39, as evaluated by ChatGPT using a five-point Likert scale. Moreover, this study identifies ChatGPT’s capability for automatic mapping and conversion from one format to the other, addition, correction and supplementation handling in MARC21 bibliographic records, as well as categorizes the types of errors observed (e.g., error and duplication).