On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model

Shin, Seongjin; Lee, Sang‐Woo; Ahn, Hwijeen; Kim, Sungdong; Kim, HyoungSeok; Kim, Boseop; Cho, Kyunghyun; Lee, Gichang; Park, Woomyoung; Ha, Jung-Woo; Sung, Nako

doi:10.48550/arxiv.2204.13509

Cited by 1 publication

(2 citation statements)

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“…It is usually difficult for LLM to perform well on domain-specific tasks as our hint-text generation, and a common practice would be employing the in-context learning [42,94,119] schema to boost the performance. It provides the LLM with examples to demonstrate what the instruction is to enable the LLM to better understand the task.…”

Section: Enriching Prompt With Examplesmentioning

confidence: 99%

“…To achieve this, we first build a basic example dataset of hint-text from the popular mobile apps in our motivational study. Research shows that the quality and relevance of examples can significantly affect the performance of LLM [42,94,119]. Therefore, based on the dataset we built, a retrieval-based example selection method (in Section 5.2.2) is designed to select the most appropriate example according to the text input and its hint-text, which further enables the LLM to learn with pertinence.…”

Section: Enriching Prompt With Examplesmentioning

confidence: 99%

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Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM

Liu,

Chen,

Wang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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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 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.

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Section: Enriching Prompt With Examplesmentioning

confidence: 99%

Section: Enriching Prompt With Examplesmentioning

confidence: 99%