1. ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
- Author
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Wu, Guande, Qian, Jing, Castelo, Sonia, Chen, Shaoyu, Rulff, Joao, and Silva, Claudio
- Subjects
Computer Science - Human-Computer Interaction ,Computer Science - Computation and Language ,H.1.2 ,I.2.7 - Abstract
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., Comment: Conditionally accepted by CHI '24
- Published
- 2024
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