ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
This addresses readability issues for users of head-mounted augmented reality displays, but it is incremental as it applies existing simplification techniques to a new domain.
The paper tackles the problem of text in augmented reality being hard to read quickly during demanding tasks by proposing ARTiST, an automatic text simplification system using GPT-3 and few-shot prompts; results from a 16-user study showed it significantly reduces cognitive load and improves performance over unmodified and traditionally modified text.
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.