@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://aclanthology.org/2025.emnlp-industry.74/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T REIC: RAG-Enhanced Intent Classification at Scale
%A Zhang, Ziji
%A Yang, Michael
%A Chen, Zhiyu
%A Zhuang, Yingying
%A Pi, Shu-Ting
%A Liu, Qun
%A Maragoud, Rajashekar
%A Nguyen, Vy
%A Beniwal, Anurag
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F zhang-etal-2025-reic
%X 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.
%R 10.18653/v1/2025.emnlp-industry.74
%U https://aclanthology.org/2025.emnlp-industry.74/
%U https://doi.org/10.18653/v1/2025.emnlp-industry.74
%P 1072-1080
Markdown (Informal)
[REIC: RAG-Enhanced Intent Classification at Scale](https://aclanthology.org/2025.emnlp-industry.74/) (Zhang et al., EMNLP 2025)
ACL
- Ziji Zhang, Michael Yang, Zhiyu Chen, Yingying Zhuang, Shu-Ting Pi, Qun Liu, Rajashekar Maragoud, Vy Nguyen, and Anurag Beniwal. 2025. REIC: RAG-Enhanced Intent Classification at Scale. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1072–1080, Suzhou (China). Association for Computational Linguistics.