Evaluating Proprietary and Open-Weight Large Language Models as Universal Decimal Classification Recommender Systems

Evaluating Proprietary and Open-Weight Large Language Models as Universal Decimal Classification Recommender Systems

Manual assignment of Universal Decimal Classification (UDC) codes is time-consuming and inconsistent as digital library collections expand. This study evaluates 17 large language models (LLMs) as UDC classification recommender systems, including ChatGPT variants (GPT-3.5, GPT-4o, and o1-mini), Claud...

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Bibliographic Details
Main Authors: Mladen Borovič, Eftimije Tomovski, Tom Li Dobnik, Sandi Majninger
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
universal decimal classification
large language models
conversational systems
recommender systems
prompt engineering
zero-shot classification
Online Access:https://www.mdpi.com/2076-3417/15/14/7666
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https://www.mdpi.com/2076-3417/15/14/7666

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