Large Language Model-Based User Modeling for Intelligent Customer Service and Query Routing
Keywords:
large language models; user modeling; intelligent customer service; query routing; intent inference; sociotechnical systemsAbstract
Intelligent customer service systems increasingly depend on accurate and adaptive representations of users to interpret requests, infer intent, and route queries to appropriate resolution pathways. The emergence of large language models has created new opportunities for building richer user models that operate over unstructured conversational evidence, historical interaction traces, and contextual signals. This paper presents a system-level examination of large language model-based user modeling for intelligent customer service and query routing. It analyzes how language model capabilities can be integrated into user profile construction, dynamic intent inference, and routing decisions across heterogeneous service ecosystems. The discussion emphasizes structural trade-offs among latency, accuracy, explainability, privacy, and operational cost. It further addresses robustness under distributional shift, fairness across demographic and linguistic groups, governance constraints, and deployment sustainability. Rather than prescribing a single architecture, the paper develops a conceptual framework that situates large language model-based user modeling within the broader sociotechnical infrastructure of customer service. It considers how retrieval-augmented generation, memory mechanisms, and parameter-efficient adaptation can support continuous user understanding while limiting the risks of overpersonalization and opaque decision-making. The analysis highlights that effective query routing requires not only accurate intent recognition but also institutional alignment, escalation policies, and accountability mechanisms. The paper concludes by identifying future research directions for more transparent, resilient, and user-centered language model-based service platforms.
References
1. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186.
2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
3. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., ... Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
4. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., ... Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
6. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
7. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Technical Report.
8. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
9. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
10. 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.
11. Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., ... Yih, W.-T. (2020). Dense passage retrieval for open-domain question answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 6769–6781.
12. 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.
13. Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., ... Wei, J. (2022). Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416.
14. Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4), 329–354.
15. Kobsa, A. (2001). Generic user modeling systems. User Modeling and User-Adapted Interaction, 11(1–2), 49–63.
16. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
17. Yu, X. (2026). Enhancing Search Efficiency through LLM-Based User Memory Systems for Query Matching and Intent Modeling.
18. Chen, H., Liu, X., Yin, D., & Tang, J. (2017). A survey on dialogue systems: Recent advances and new frontiers. ACM SIGKDD Explorations Newsletter, 19(2), 25–35.
19. Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021). Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 624–635.
20. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
21. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
22. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503–2511.
23. Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., ... Gelly, S. (2019). Parameter-efficient transfer learning for NLP. Proceedings of the 36th International Conference on Machine Learning, 2790–2799.
24. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., ... Chen, W. (2022). LoRA: Low-rank adaptation of large language models. Proceedings of the International Conference on Learning Representations.
25. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... Rush, A. M. (2020). Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38–45.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Computer Science and Engineering Transactions
This work is licensed under a Creative Commons Attribution 4.0 International License.