11 citations · 14 across the 11 of their papers we have counts for
5 papers · 1 filter
LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles
Ho Yin 'Sam' Ng, Ting-Yao Hsu, Aashish Anantha Ramakrishnan +8
Figure captions are crucial for helping readers understand and remember a figure's key message. Many models have been developed to generate these captions, helping authors compose…
AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents
Yuxuan Lu, Ting-Yao Hsu, Hansu Gu +9
A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications. Yet, traditional A/B testing remains constrained by its dependen…
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023
Ting-Yao E. Hsu, Yi-Li Hsu, Shaurya Rohatgi +8
Since the SciCap datasets launch in 2021, the research community has made significant progress in generating captions for scientific figures in scholarly articles. In 2023, the fir…
Understanding How Paper Writers Use AI-Generated Captions in Figure Caption Writing
Ho Yin, Ng, Ting-Yao Hsu +6
Figures and their captions play a key role in scientific publications. However, despite their importance, many captions in published papers are poorly crafted, largely due to a lac…
Multi-LLM Collaborative Caption Generation in Scientific Documents
Jaeyoung Kim, Jongho Lee, Hong-Jun Choi +8
Scientific figure captioning is a complex task that requires generating contextually appropriate descriptions of visual content. However, existing methods often fall short by utili…