ChatGPT not Useful as a Tool to Streamline Library Cataloguing Processes
Bibliographic record
Abstract
A Review of: Brzustowicz, R. (2023). From ChatGPT to CatGPT: The Implications of Artificial Intelligence on Library Cataloging. Information Technology and Libraries, 42(3). https://doi.org/10.5860/ital.v42i3.16295 Objective – To evaluate the potential of ChatGPT as a tool for improving efficiency and accuracy in cataloguing library records. Design – Observational, descriptive study. Setting – Online, using ChatGPT and the WorldCat catalogue. Subject – The Large Language Model (LLM) ChatGPT. Methods – Prompting ChatGPT to create MARC records for items in different formats and languages and comparing the ChatGPT derived records versus those obtained from the WorldCat catalogue. Main results – ChatGPT was able to generate MARC records, but the accuracy of the records was questionable, despite the authors’ claims. Conclusion – Based on the results of this study, the author concludes that using ChatGPT to streamline the process of cataloging could allow library staff to focus time and energy on other types of work. However, the results presented suggest that ChatGPT introduces significant errors in the MARC records created, thereby requiring additional time for cataloguers to correct the error-laden records. The author correctly stresses that if ChatGPT were used to assist with cataloguing, it would remain important for professionals to check the records for completion and accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.171 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".