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Comparative analysis of large language models’ performance in book classification tasks using Library of Congress Classification system

Xiaoying Song et al.

Journal of Information Science2026https://doi.org/10.1177/01655515261425547article
AJG 2
Weight
0.50

What the paper says

The advancement of artificial intelligence (AI) shows the potential to facilitate information organisation, providing opportunities for classification applications in libraries. While previous studies have investigated the potential usage of AI in libraries, its real-world integration into specific tasks like cataloguing is limited. In our study, we employ large language models (LLMs) to develop classifiers for book classification using the Library of Congress Classification system. We experiment with different input data, data sizes and models to identify effective settings for assisting cataloguing using LLMs. In addition, we evaluate the model performance at different levels of Library of Congress Classification (LCC) system class granularity. The results show that Llama3 outperforms the other five models in our experiment. More diverse input data may contribute to enhancing model performance. However, LLMs demonstrate limitations in processing long text and handling more granular categories.

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https://doi.org/https://doi.org/10.1177/01655515261425547

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@article{xiaoying2026,
  title        = {{Comparative analysis of large language models’ performance in book classification tasks using Library of Congress Classification system}},
  author       = {Xiaoying Song et al.},
  journal      = {Journal of Information Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/01655515261425547},
}

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Comparative analysis of large language models’ performance in book classification tasks using Library of Congress Classification system

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.