Abstract
Since its transformation into Taiwan’s first national digital library in 2013, the National Library of Public Information (NLPI) has continuously adopted innovative technologies to advance intelligent services. In response to the rise of large language models (LLMs), NLPI launched the “Generative AI Virtual Librarian” project in 2023 and developed Xiaoshu, a virtual librarian capable of natural voice interaction. Centered on generative AI, the system integrates four databases and eight intent categories, applying retrieval-augmented generation (RAG) and speech recognition to provide collection search, book recommendations, service inquiries, and social interaction.
Xiaoshu effectively handles a large number of repetitive yet diverse library inquiries, demonstrating the linguistic flexibility of generative AI. Compared with rule-based systems that rely on extensive pre-set Q&A pairs, generative AI reduces maintenance workload by shifting the focus from data quantity to data quality. From late 2023 to October 2025, Xiaoshu recorded over 70,000 interactions, serving about 3,000 users monthly with an accuracy rate above 80%, and reducing human librarian workloads by approximately 16 hours per month.
The project highlights the importance of defining clear service goals, user scenarios, and resource planning in the early stages. Overall, NLPI’s experience shows that generative AI librarians can enhance service efficiency and create a new paradigm for human–AI collaboration in public libraries.
References
Hsieh, H.-Y., Ko, C.-C., Yu, L.-K., Chen, T.-E., Jwo, J.-S., Li, Y.-C., Chang, H.-C., & Ma, H.-P. (2024). 智慧圖書館:基於 GPT-4 的智慧館員 [Smart Library: Intelligent Librarian Powered by GPT-4]. 公共圖書館研究, 19, 10-14.
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., & McHardy, R. (2023). Challenges and applications of large language models. arXiv preprint arXiv:2307.10169. https://arxiv.org/abs/2307.10169
Lew, G., & Schumacher, R. M. (2020). AI and UX: Why artificial intelligence needs user experience. Apress.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
Liu, Z.-E. (2024). 聊天機器人之讀者體驗研究:以國立公共資訊圖書館為例 [A study of reader experience with chatbots: A case study of the National Library of Public Information]. In 2024 圖書資訊學術與實務研討會會議論文集 (pp. 199-206). Library Association of the Republic of China (Taiwan).
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., & Hu, X. (2024). Harnessing the power of LLMs in practice: A survey on ChatGPT and beyond. ACM Transactions on Knowledge Discovery from Data, 18(6), 1–32. https://doi.org/10.1145/3649506
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., & Wen, J. R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223. https://arxiv.org/abs/2303.18223
Zheng, Q., Tang, Y., Liu, Y., Liu, W., & Huang, Y. (2022, April). UX research on conversational human-AI interaction: A literature review of the ACM Digital Library. In S. Barbosa & C. Lampe (Eds.), Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-24). Association for Computing Machinery. https://doi.org/10.1145/3491102.3501855

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 International Journal of Librarianship
