AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing
Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a significant challenge for even state-of-the-art Large Language Models (LLMs). A lack of task-specific data often limits previous works, and direct LLM application suffers from strategic brittleness and factual hallucination. In this paper, we first construct and release TeleSalesCorpus, the first real-world-grounded dialogue dataset for this domain. We then propose AI-Salesman, a novel framework featuring a dual-stage architecture. For the training stage, we design a Bayesian-supervised reinforcement learning algorithm that learns robust sales strategies from noisy dialogues. For the inference stage, we introduce the Dynamic Outline-Guided Agent (DOGA), which leverages a pre-built script library to provide dynamic, turn-by-turn strategic guidance. Moreover, we design a comprehensive evaluation framework that combines fine-grained metrics for key sales skills with the LLM-as-a-Judge paradigm. Experimental results demonstrate that our proposed AI-Salesman significantly outperforms baseline models in both automatic metrics and comprehensive human evaluations, showcasing its effectiveness in complex persuasive scenarios.
691be3716bfd5965c0fd37c4
Qingyu Zhang
claimed_verified
2025-11-18T14:08:23.505Z
6520c14d06fa58638b441082
/avatars/1259adca184128ab2af052d136a64400.svg
Qingyu Zhang
zhangqingyu
user
zhangqingyu
691be3716bfd5965c0fd37c5
Chunlei Xin
691be3716bfd5965c0fd37c6
Xuanang Chen
691be3716bfd5965c0fd37c7
Yaojie Lu
691be3716bfd5965c0fd37c8
Hongyu Lin
691be3716bfd5965c0fd37c9
Xianpei Han
691be3716bfd5965c0fd37ca
Le Sun
691be3716bfd5965c0fd37cb
Qing Ye
691be3716bfd5965c0fd37cc
Qianlong Xie
691be3716bfd5965c0fd37cd
Xingxing Wang
AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing
Goal-driven persuasive dialogue, exemplified by applications like telemarketing, requires sophisticated multi-turn planning and strict factual faithfulness, which remains a significant challenge for even state-of-the-art Large Language Models (LLMs). A lack of task-specific data often limits previous works, and direct LLM application suffers from strategic brittleness and factual hallucination. In this paper, we first construct and release TeleSalesCorpus, the first real-world-grounded dialogue dataset for this domain. We then propose AI-Salesman, a novel framework featuring a dual-stage architecture. For the training stage, we design a Bayesian-supervised reinforcement learning algorithm that learns robust sales strategies from noisy dialogues. For the inference stage, we introduce the Dynamic Outline-Guided Agent (DOGA), which leverages a pre-built script library to provide dynamic, turn-by-turn strategic guidance. Moreover, we design a comprehensive evaluation framework that combines fine-grained metrics for key sales skills with the LLM-as-a-Judge paradigm. Experimental results demonstrate that our proposed AI-Salesman significantly outperforms baseline models in both automatic metrics and comprehensive human evaluations, showcasing its effectiveness in complex persuasive scenarios.