DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition

• URL: http://arxiv.org/abs/2605.17900v1
• Date: Mon, 18 May 2026 06:06:01 GMT
• Title: DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition
• Abstract summary: We present a large language model (LLM)-based end-to-end framework designed for large-scale POI attribute acquisition at Baidu Maps.<n>DuIVRS-2 processed 0.4 million calls daily and achieved a 83.9% Task Success Rate (TSR)<n>This work provides a production-proven reference for developing robust, cost-effective LLM agents for large-scale industrial dialogue applications.
• Score: 28.52285542771737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional modular Interactive Voice Response (IVR) systems suffer from error accumulation and high maintenance overhead. We present DuIVRS-2, a large language model (LLM)-based end-to-end framework designed for large-scale POI attribute acquisition at Baidu Maps. To address the long-tail distribution of real-world interactions, our methodology first employs a finite state machine (FSM)-guided data augmentation strategy to synthesize a balanced and diverse training dataset. We then streamline dialogue management via a selective generation scheme combined with a Chain-of-Thought (CoT) mechanism, which ensures output stability and effectively eliminates hallucinations in industrial settings. To facilitate continuous policy refinement with minimal manual effort, we design a cooperative iterative learning framework that leverages a dual-evaluator voting system. Deployed in production for two months, DuIVRS-2 processed 0.4 million calls daily and achieved a 83.9\% Task Success Rate (TSR), outperforming its predecessor by 4 percentage points while maintaining a low reaction time of 130ms. This work provides a production-proven reference for developing robust, cost-effective LLM agents for large-scale industrial dialogue applications.

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