Creating, Using and Assessing a Generative-AI-Based Human-Chatbot-Dialogue Dataset with User-Interaction Learning Capabilities
Year: 2024
Type: conference-paper
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.
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: NextGenerationEU
OpenAlex ID: W4406460017