Enhancing Multi-Person Dialogue with Large Language Models: A Structured Approach to Natural Communication
Year: 2024
Type: conference-paper
Abstract: The rise of social networking services has increased text-based communication, often leading to misunderstandings. This study aims to develop a system using large language models (LLMs) like ChatGPT to provide real-time support in human dialogues. Traditional LLM chatbots, designed for one-on-one interactions, struggle with multi-person conversations, often leading to unnatural responses. This research proposes methods to enhance LLM's ability to distinguish between different speakers and improve its reasoning capabilities. By implementing "conversation structure tags" and simulating multi-person arguments, the system aims to generate natural, context-aware responses, enhancing the dialogue's quality and engagement.
Authors: Takumi Murogaki, Toshikazu Nishimura
Institution: Ritsumeikan University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409403790