AI销售员：构建可信赖的大语言模型驱动电话营销系统

摘要

面向目标驱动的说服性对话（如电话销售场景）需要复杂多轮规划与严格事实遵循，这对当前最先进的大语言模型仍构成重大挑战。现有研究常受限于领域数据匮乏，而直接应用大语言模型存在策略脆弱性和事实幻觉问题。本文首先构建并发布了业界首个基于真实场景的电话销售对话数据集TeleSalesCorpus，继而提出具有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，从含噪对话中学习稳健销售策略；在推理阶段，引入动态大纲引导智能体（DOGA），通过预建脚本库实现动态分轮次策略指导。此外，我们构建了结合细粒度销售技能指标与LLM-as-a-Judge范式的综合评估体系。实验表明，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卓越效能。

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销售员：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

Support