Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness

• URL: http://arxiv.org/abs/2406.17531v1
• Date: Tue, 25 Jun 2024 13:15:36 GMT
• Title: Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness
• Authors: Lucrezia Grassi, Carmine Tommaso Recchiuto, Antonio Sgorbissa,
• Abstract summary: 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. To assess the system's performance, we conducted both controlled and real-world experiments, measuring a wide range of performance indicators.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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