Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM
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arXiv
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| Format: | Preprint |
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2024
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| author | Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Huang, Yuekai Hu, Jun Wang, Qing |
| author_facet | Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Huang, Yuekai Hu, Jun Wang, Qing |
| contents | Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind visually impaired users what to fill in. Unfortunately, based on our analysis of 4,501 Android apps with text inputs, over 0.76 of them are missing hint-text. These issues are mostly caused by developers' lack of awareness when considering visually impaired individuals. To overcome these challenges, we developed an LLM-based hint-text generation model called HintDroid, which analyzes the GUI information of input components and uses in-context learning to generate the hint-text. To ensure the quality of hint-text generation, we further designed a feedback-based inspection mechanism to further adjust hint-text. The automated experiments demonstrate the high BLEU and a user study further confirms its usefulness. HintDroid can not only help visually impaired individuals, but also help ordinary people understand the requirements of input components. HintDroid demo video: https://youtu.be/FWgfcctRbfI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02706 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Huang, Yuekai Hu, Jun Wang, Qing Human-Computer Interaction Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind visually impaired users what to fill in. Unfortunately, based on our analysis of 4,501 Android apps with text inputs, over 0.76 of them are missing hint-text. These issues are mostly caused by developers' lack of awareness when considering visually impaired individuals. To overcome these challenges, we developed an LLM-based hint-text generation model called HintDroid, which analyzes the GUI information of input components and uses in-context learning to generate the hint-text. To ensure the quality of hint-text generation, we further designed a feedback-based inspection mechanism to further adjust hint-text. The automated experiments demonstrate the high BLEU and a user study further confirms its usefulness. HintDroid can not only help visually impaired individuals, but also help ordinary people understand the requirements of input components. HintDroid demo video: https://youtu.be/FWgfcctRbfI. |
| title | Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.02706 |