Abstract
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.
Abstract (translated)
近年来，在对话系统和语料库研究中，主要集中于两个主要类别：任务导向对话（TOD）和开放域对话（CHIT-CHAT）。TOD系统帮助用户完成特定任务，而开放域系统旨在创建有趣的对话。然而，在现实世界的场景中，用户意图通常在互动过程中揭示。一项最近的研究引入了SalesBot，它通过模拟从CHIT-CHAT到任务导向场景的对话过渡来训练销售代理。然而，最初的数据显示，数据缺乏平滑的过渡和连贯的长轮对话，导致销售-客户互动的自然度较差。为了解决这些问题，本文提出了SalesBot 2.0，一个改进的数据集。它通过大规模语言模型（LLMs）的常识知识来进行策略提示。此外，我们引入了一种名为SalesAgent的新模型，使用链式思维（CoT）进行训练。这种模型在转移主题、理解用户意图和选择适当的策略方面表现出色。使用多样化的用户模拟实验证实了我们在LLMs上控制对话策略的有效性。此外，SalesBot 2.0还提高了连贯性并减少了攻击性，有助于提高模型学习销售-客户互动的效果。
URL
https://arxiv.org/abs/2404.18564
