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    <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.</abstract>
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%0 Conference Proceedings
%T DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition
%A Zhang, Le
%A Zhang, Shengming
%A Zha, Rui
%A Yunpeng, Wu
%A Zhou, Jingbo
%A Huang, Jizhou
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zhang-etal-2026-duivrs
%X 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.
%U https://aclanthology.org/2026.acl-industry.44/
%P 632-645

Markdown (Informal)

[DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition](https://aclanthology.org/2026.acl-industry.44/) (Zhang et al., ACL 2026)

• DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition (Zhang et al., ACL 2026)

ACL

• Le Zhang, Shengming Zhang, Rui Zha, Wu Yunpeng, Jingbo Zhou, and Jizhou Huang. 2026. DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 632–645, San Diego, California, USA. Association for Computational Linguistics.