RelBot: Building a Balanced Relationship From Conversation Content

To construct a conversational system for robots that considers the interpersonal relationships among three parties, this paper proposes a system called RelBot. The system uses a large language model (LLM) to estimate the current interpersonal relationship and ideal balanced interpersonal relationship from the content of a three-way conversation between a user and two robots; then, it generates statements for the robots to establish the ideal balanced relationship according to the user's desire and Heider's balance theory. The challenging points here are to investigate whether an LLM can recognize the current relationships between a user and each robot and between two robots from the content of their conversation, and whether it can estimate the relationships that the user wants to achieve. Moreover, RelBot provides a new way to adjust the three-way relationship to the ideal balanced one. We conducted two evaluation experiments. The results indicated that participants could intentionally change the relationships to the desired ones, while RelBot accurately recognized both the current relationship and the participant's desired relationship. Furthermore, the final relationship in the free conversation with two robots could be balanced through the effectiveness of RelBot's generated statements.