利用细微差异增强基于LLM的人机交互以提升多样性意识
Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness
AI总结：
本文提出一种基于LLM的多样性感知自主对话系统，通过知识库引导对话流程并利用精心设计的提示生成多样性感知语句，经实验验证能有效适应不同背景的个体。
摘要：
本文提出了一种利用大型语言模型（LLMs）能力的多样性感知自主对话系统。该系统适应多样化的人群和个体，考虑背景、性格、年龄、性别和文化等因素。对话流程由系统预先建立的知识库结构引导，同时LLMs承担多项功能，包括生成具有多样性意识的句子。实现多样性感知需要向模型提供精心设计的提示，整合用户信息、对话历史、上下文细节和具体指南。为评估系统性能，我们进行了受控和真实世界实验，测量了广泛的性能指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
