Papers by Jason Wu
Multi-Dimensional Gender Bias Classification (2020.emnlp-main)
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| Challenge: | a novel framework decomposes gender bias in text along several pragmatic and semantic dimensions . language is a primary means by which people communicate, express identities and categorize themselves . unwanted gender biases can affect downstream applications, leading to poor user experiences . |
| Approach: | They propose a framework that decomposes gender bias in text along several dimensions . they annotate eight large scale datasets with gender information and collect a benchmark . |
| Outcome: | The proposed framework decomposes gender bias in text along several pragmatic and semantic dimensions. |
UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback (2024.naacl-long)
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| Challenge: | Existing approaches to improve UI code generation rely on expensive human feedback or distilling a proprietary model. |
| Approach: | They propose to use automated feedback to guide large language models to generate UI code . they use a large synthetic dataset to generate improved models and refine them . |
| Outcome: | The proposed model outperforms baseline models and larger proprietary models . the model outpersforms models with automated metrics and human preferences . |