AI推销员：构建可信赖的大语言模型驱动电话营销系统

摘要

以电话营销为代表的目标驱动型说服性对话，需要复杂的多轮规划与严格的事实遵循，这对当前最先进的大语言模型仍构成重大挑战。以往研究常受限于任务特定数据的匮乏，而直接应用大语言模型则存在策略脆弱性和事实幻觉问题。本文首次构建并发布了该领域首个基于真实场景的对话数据集TeleSalesCorpus，进而提出具备双阶段架构的创新框架AI-Salesman。在训练阶段，我们设计了贝叶斯监督强化学习算法，从含噪声对话中学习鲁棒的销售策略；在推理阶段，我们引入动态大纲引导智能体（DOGA），通过预构建脚本库提供动态的逐轮策略指导。此外，我们设计了结合细粒度销售技能指标与LLM即评判范式的综合评估框架。实验结果表明，所提出的AI-Salesman在自动指标和综合人工评估中均显著优于基线模型，展现了其在复杂说服场景中的有效性。

English

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.

AI推销员：构建可信赖的大语言模型驱动电话营销系统

AI-Salesman: Towards Reliable Large Language Model Driven Telemarketing

摘要

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