Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI)
has been gaining considerable attention in research in recent years. However,
the HRI community lacks a set of shared terminology and framework for
characterizing aspects of mixed reality interfaces, presenting serious problems
for future research. Therefore, it is important to have a common set of terms
and concepts that can be used to precisely describe and organize the diverse
array of work being done within the field. In this paper, we present a novel
taxonomic framework for different types of VAM-HRI interfaces, composed of four
main categories of virtual design elements (VDEs). We present and justify our
taxonomy and explain how its elements have been developed over the last 30
years as well as the current directions VAM-HRI is headed in the coming decade.

本文提出了一个包括四大类虚拟设计元素的分类框架，用于描述和组织 VAM-HRI 界面类型，旨在解决 HRI 社区缺乏共享术语和框架的问题，以及说明该框架如何在未来的十年内发展。

人机交互的虚拟、增强和混合现实：调研和虚拟设计元素分类

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy

In this paper, we leverage large language models (LMs) to perform zero-shot
text style transfer. We present a prompting method that we call augmented
zero-shot learning, which frames style transfer as a sentence rewriting task
and requires only a natural language instruction, without model fine-tuning or
exemplars in the target style. Augmented zero-shot learning is simple and
demonstrates promising results not just on standard style transfer tasks such
as sentiment, but also on arbitrary transformations such as "make this
melodramatic" or "insert a metaphor."

本文采用大型语言模型（LM）来进行零样式转移，提出了一种提示方法，称为增强的零样式学习，将样式转移作为句子重写任务，并仅需要自然语言指令，无需目标样式的模型微调或示例。增强的零样式学习简单易用，不仅在情感等标准样式转换任务上表现出色，而且可以进行任意变换，例如 “使其多愁善感” 或 “插入一个隐喻”。