8 citations · 14 across the 5 of their papers we have counts for
5 papers
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Yuxuan Lu, Bingsheng Yao, Shao Zhang +5
Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domai…
UI Layout Generation with LLMs Guided by UI Grammar
Yuwen Lu, Ziang Tong, Qinyi Zhao +2
The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mob…
Impact of Human-AI Interaction on User Trust and Reliance in AI-Assisted Qualitative Coding
Jie Gao, Junming Cao, ShunYi Yeo +5
While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop…
Modeling Programmer Attention as Scanpath Prediction
Aakash Bansal, Chia-Yi Su, Zachary Karas +4
This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performin…
PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data
Zheng Zhang, Zheng Ning, Chenliang Xu +2
Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downst…