Investigating the Intervention in Parallel Conversations

In recent years, a framework of parallel conversations has been proposed to facilitate efficient conversations through cooperation between humans and dialogue systems. This approach aims to enable simultaneous conversations with multiple users by enabling the system to handle basic conversation and human operators to intervene when problems arise in the system’s conversation. Previous studies on parallel conversations have primarily focused on delegating simple exchanges such as greetings and acknowledgments to the system, with humans taking over for more complex interactions like providing guidance. Recent advancements in large language models may change this situation, enabling dialogue systems to engage in more advanced interactions. In this study, to examine which interventions will be made when large language models are utilized, we placed six dialogue robots based on large language models in an actual facility and conducted a field experiment involving parallel conversations for about a month. Our analysis of the collected data on dialogues and interventions showed that the most frequent interventions were made for supporting interactions when the system failed to react to the user utterances, indicating the limitations of using large language models alone and clarifying our next steps for facilitating smoother parallel conversations.