Recent research in dialogue systems and corpora has focused on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users accomplish specific tasks, while open-domain systems aim to create engaging conversations. However, in real-world scenarios, user intents are often revealed during interactions. A recent study introduced SalesBot, which simulates dialogues transitioning from chit-chat to task-oriented scenarios to train sales agents. Unfortunately, the initial data lacked smooth transitions and coherent long-turn dialogues, resulting in poor naturalness in sales-customer interactions. To address these issues, this paper presents SalesBot 2.0, an improved dataset. It leverages commonsense knowledge from large language models (LLMs) through strategic prompting. Additionally, we introduce a novel model called SalesAgent, trained on salesperson's interactions, using chain-of-thought (CoT) reasoning. This model excels in transitioning topics, understanding user intents, and selecting appropriate strategies. Experiments using diverse user simulations validate the effectiveness of our method in controlling dialogue strategies in LLMs. Furthermore, SalesBot 2.0 enhances coherence and reduces aggression, facilitating better model learning for sales-customer interactions.

翻译：近期对话系统与语料库研究聚焦两大类别：任务导向型对话与开放域（闲聊）对话。任务导向型系统帮助用户完成特定任务，开放域系统则致力于营造沉浸式对话体验。然而现实场景中，用户意图往往在交互过程中才会显现。最新研究推出的SalesBot系统模拟了从闲聊向任务场景过渡的对话训练销售人员，但初始数据存在过渡不自然、长程对话连贯性不足等问题，导致销售-客户交互的自然度欠佳。为解决这些问题，本文提出改进数据集SalesBot 2.0，通过策略性提示技术利用大语言模型的常识知识。此外，我们创新性地提出基于销售人员交互训练的SalesAgent模型，该模型采用思维链推理机制，在话题转换、用户意图理解及策略选择方面表现优异。通过多样化用户仿真实验，验证了该方法在控制大语言模型对话策略方面的有效性。值得一提的是，SalesBot 2.0在提升对话连贯性、降低对抗性的同时，为销售-客户交互的模型学习创建了更优质的训练基础。