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.

通过利用大规模语言模型的能力，本文提出了一套针对多样化感知的自主对话系统，该系统适应不同人群和个体，考虑到背景、个性、年龄、性别和文化等因素，对话过程由系统预设的知识库架构引导，并利用语言模型生成多样化感知的句子，通过提供精心设计的提示，融合了关于用户、对话历史、语境细节和具体指导的综合信息。我们进行了受控实验和现实世界实验来评估系统的性能，测量了一系列性能指标。

以细微之处增强基于 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

The article introduces the concept of "diversity-aware" robotics and
discusses the need to develop computational models to embed robots with
diversity-awareness: that is, robots capable of adapting and re-configuring
their behavior to recognize, respect, and value the uniqueness of the person
they interact with to promote inclusion regardless of their age, race, gender,
cognitive or physical capabilities, etc. Finally, the article discusses
possible technical solutions based on Ontologies and Bayesian Networks,
starting from previous experience with culturally competent robots.

介绍了 “多元文化感知” 机器人的概念，讨论了需要开发计算模型来嵌入具有多元文化意识的机器人的必要性，最终讨论了可能的技术解决方案，基于本体论和贝叶斯网络，以前一个具有多元文化能力的机器人为例。