用大模型让客服语音系统理解自然语言，自动跳转到正确服务路径。
Beyond IVR Touch-Tones: Customer Intent Routing using LLMs
- 用大模型生成23个节点的虚拟客服菜单和920条用户意图
- 扁平化路径表示法准确率达89.13%，优于分层描述法的81.30%
- 适合想升级传统语音菜单的企业或研究者
现有客服语音系统（IVR）依赖僵化的按键交互，用户体验差。本文提出基于大语言模型（LLM）的意图路由方法，解决自然语言到菜单路径的映射难题。通过三类模型构建了包含23个节点的真实感虚拟IVR结构，生成920条用户意图（230条基础+690条增强）。在两种提示设计下评估：分层菜单描述与扁平化路径表示。结果显示，扁平化路径在基础数据集上准确率达89.13%，高于分层格式的81.30%；而增强数据引入语言噪声，轻微降低性能。混淆矩阵分析表明，部分低效路由可能源于菜单设计冗余，非仅模型能力问题。实验验证了大模型实现无缝自然语言交互的可行性，推动客服系统向更智能方向演进。
原文摘要 · Abstract (English)
Widespread frustration with rigid touch-tone Interactive Voice Response (IVR) systems for customer service underscores the need for more direct and intuitive language interaction. While speech technologies are necessary, the key challenge lies in routing intents from user phrasings to IVR menu paths, a task where Large Language Models (LLMs) show strong potential. Progress, however, is limited by data scarcity, as real IVR structures and interactions are often proprietary. We present a novel LLM-based methodology to address this gap. Using three distinct models, we synthesized a realistic 23-node IVR structure, generated 920 user intents (230 base and 690 augmented), and performed the routing task. We evaluate two prompt designs: descriptive hierarchical menus and flattened path representations, across both base and augmented datasets. Results show that flattened paths consistently yield higher accuracy, reaching 89.13% on the base dataset compared to 81.30% with the descriptive format, while augmentation introduces linguistic noise that slightly reduces performance. Confusion matrix analysis further suggests that low-performing routes may reflect not only model limitations but also redundancies in menu design. Overall, our findings demonstrate proof-of-concept that LLMs can enable IVR routing through a smoother, more seamless user experience -- moving customer service one step ahead of touch-tone menus.
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