用检索增强生成解决客服意图分类的规模化难题
REIC: RAG-Enhanced Intent Classification at Scale
- 通过RAG动态引入外部知识，避免频繁重训
- 在真实数据集上优于传统微调与零样本方法
- 适合需要快速适应新产品的客服系统
准确的意图分类对客户服务中心的高效路由至关重要，可确保客户被分配给最合适的客服人员，从而降低处理时间和运营成本。然而，随着企业产品线扩展，意图数量增加及各垂直领域分类体系差异导致分类系统面临规模化挑战。本文提出REIC——一种基于检索增强生成（RAG）的意图分类方法，通过动态引入相关知识，实现无需频繁重训的精准分类。在真实世界数据集上的大量实验表明，REIC在大规模客服场景中显著优于传统微调、零样本和少样本方法。结果证明其在域内与域外场景均具有效性，具备在自适应、大规模意图分类系统中部署的潜力。
原文摘要 · Abstract (English)
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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