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ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality

arXiv 2402.18797 ↗First posted 29 Feb 2024cs.HC cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 53 of its 317 references that are in the map. First posted on arXiv 29 Feb 2024.

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References · 53

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