Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods. Our work constitutes a step towards automating the optimization of batch text data for readability and performance in augmented reality.

翻译：增强现实中呈现的文本能为用户提供原位实时信息，但在执行高认知负荷的增强现实任务时（尤其是采用头戴式显示器呈现时），这类内容可能难以快速理解。我们提出ARTiST系统，这是一个利用少样本提示与GPT-3模型自动优化增强现实文本长度与语义内容的文本简化系统。该系统基于包含七名用户与三名专家的形成性研究开发，通过将定制化误差校准模型与少样本提示相结合，整合句法、词汇、阐释及内容简化技术，为头戴式显示器生成简化后的增强现实文本。基于16名用户的实证研究表明，相较于未修改文本及传统方法修改的文本，ARTiST显著降低了认知负荷并提升了任务表现。本研究向实现增强现实中批量文本数据的可读性及性能自动化优化迈出了关键一步。