  This paper presents a system for diversity-aware autonomous conversation
leveraging the capabilities of large language models (LLMs). The system adapts
to diverse populations and individuals, considering factors like background,
personality, age, gender, and culture. The conversation flow is guided by the
structure of the system's pre-established knowledge base, while LLMs are tasked
with various functions, including generating diversity-aware sentences.
Achieving diversity-awareness involves providing carefully crafted prompts to
the models, incorporating comprehensive information about users, conversation
history, contextual details, and specific guidelines. To assess the system's
performance, we conducted both controlled and real-world experiments, measuring
a wide range of performance indicators.
