ARTiST: Automated Text Simplification for Task Guidance in Augmented
  Reality
Guande Wu Jing Qian Sonia Castelo Shaoyu Chen Joao Rulff Claudio Silva
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.