ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909123238428672 |
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| author | Wu, Guande Qian, Jing Castelo, Sonia Chen, Shaoyu Rulff, Joao Silva, Claudio |
| author_facet | Wu, Guande Qian, Jing Castelo, Sonia Chen, Shaoyu Rulff, Joao Silva, Claudio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality Wu, Guande Qian, Jing Castelo, Sonia Chen, Shaoyu Rulff, Joao Silva, Claudio Human-Computer Interaction Computation and Language H.1.2; I.2.7 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. |
| title | ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality |
| topic | Human-Computer Interaction Computation and Language H.1.2; I.2.7 |
| url | https://arxiv.org/abs/2402.18797 |