Title: Creating, Using and Assessing a Generative-AI-Based Human-Chatbot-Dialogue Dataset with User-Interaction Learning Capabilities Show Chinese title

Title: 创建、使用和评估具有用户交互学习能力的生成式人工智能人机对话数据集

Abstract: The study illustrates a first step towards an ongoing work aimed at developing a dataset of dialogues potentially useful for customer service conversation management between humans and AI chatbots. The approach exploits ChatGPT 3.5 to generate dialogues. One of the requirements is that the dialogue is characterized by a specific language proficiency level of the user; the other one is that the user expresses a specific emotion during the interaction. The generated dialogues were then evaluated for overall quality. The complexity of the language used by both humans and AI agents, has been evaluated by using standard complexity measurements. Furthermore, the attitudes and interaction patterns exhibited by the chatbot at each turn have been stored for further detection of common conversation patterns in specific emotional contexts. The methodology could improve human-AI dialogue effectiveness and serve as a basis for systems that can learn from user interactions. Show Chinese abstract

Abstract: 这项研究展示了朝着持续工作迈出的第一步，旨在开发一个对话数据集，可能对人类与AI聊天机器人之间的客户服务对话管理有用。 该方法利用ChatGPT 3.5生成对话。 其中一个要求是对话具有用户特定的语言熟练程度；另一个要求是用户在交互过程中表达特定的情绪。 然后对生成的对话进行了整体质量评估。 通过使用标准复杂性测量方法，评估了人类和AI代理使用的语言的复杂性。 此外，聊天机器人在每一轮中表现出的态度和互动模式已被存储，以便进一步检测特定情绪情境下的常见对话模式。 该方法可以提高人机对话的有效性，并作为能够从用户交互中学习的系统的基础。