# REIC: RAG-Enhanced Intent Classification at Scale

arXiv: https://arxiv.org/abs/2506.00210 · HTML: https://mlsanity.com/paper/2506.00210/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 21 of 21)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion](https://mlsanity.com/paper/2203.13224/) (2022)
- [Few-shot Intent Classification and Slot Filling with Retrieved Examples](https://mlsanity.com/paper/2104.05763/) (2021)
- [Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection](https://mlsanity.com/paper/2010.02481/) (2020)
- [LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification](https://mlsanity.com/paper/2403.16504/) (2024)
- [DARD: A Multi-Agent Approach for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2411.00427/) (2024)
- [Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations](https://mlsanity.com/paper/2012.03929/) (2020)
- [Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers](https://mlsanity.com/paper/2406.05255/) (2024)
- [Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search](https://mlsanity.com/paper/2306.03411/) (2023)
- [JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search](https://mlsanity.com/paper/2005.13783/) (2020)
- [Identifying High Consideration E-Commerce Search Queries](https://mlsanity.com/paper/2410.13951/) (2024)
- [Universal Model in Online Customer Service](https://mlsanity.com/paper/2402.15666/) (2024)
- [Uncovering Customer Issues through Topological Natural Language Analysis](https://mlsanity.com/paper/2403.00804/) (2024)

## Built on by (most cited first, 3 of 3)

- [How and Where to Translate? The Impact of Translation Strategies in Cross-lingual LLM Prompting](https://mlsanity.com/paper/2507.22923/) (2025)
- [Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study](https://mlsanity.com/paper/2603.01486/) (2026)
- [Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs](https://mlsanity.com/paper/2609.27262/) (2026)

## Abstract (arXiv)

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.