Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness
This addresses the challenge of making AI interactions more inclusive for diverse populations, though it appears incremental as it builds on existing LLM capabilities with tailored prompts.
The paper tackles the problem of enabling large language models to conduct diversity-aware conversations in human-robot interaction by adapting to factors like background and culture, and reports performance improvements through controlled and real-world experiments.
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.