@inproceedings{zhang-etal-2025-reic,
    title = "{REIC}: {RAG}-Enhanced Intent Classification at Scale",
    author = "Zhang, Ziji  and
      Yang, Michael  and
      Chen, Zhiyu  and
      Zhuang, Yingying  and
      Pi, Shu-Ting  and
      Liu, Qun  and
      Maragoud, Rajashekar  and
      Nguyen, Vy  and
      Beniwal, Anurag",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.74/",
    pages = "1072--1080",
    ISBN = "979-8-89176-333-3",
    abstract = "Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their product lines, intent classification faces scalability challenges due to the increasing number of intents and variations in taxonomy across different verticals. In this paper, we introduce REIC, a Retrieval-augmented generation Enhanced Intent Classification approach, which addresses these challenges effectively. REIC leverages retrieval-augmented generation (RAG) to dynamically incorporate relevant knowledge, enabling precise classification without the need for frequent retraining. Through extensive experiments on real-world datasets, we demonstrate that REIC outperforms traditional fine-tuning, zero-shot, and few-shot methods in large-scale customer service settings. Our results highlight its effectiveness in both in-domain and out-of-domain scenarios, demonstrating its potential for real-world deployment in adaptive and large-scale intent classification systems."
}