11 citations · 14 across the 11 of their papers we have counts for
13 papers
Five Years of SciCap: What We Learned and Future Directions for Scientific Figure Captioning
Ting-Hao 'Kenneth' Huang, Ryan A. Rossi, Sungchul Kim +5
Between 2021 and 2025, the SciCap project grew from a small seed-funded idea at The Pennsylvania State University (Penn State) into one of the central efforts shaping the scientifi…
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…
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang +5
Crafting effective captions for figures is important. Readers heavily depend on these captions to grasp the figure's message. However, despite a well-developed set of AI technologi…
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi +3
There is growing interest in systems that generate captions for scientific figures. However, assessing these systems output poses a significant challenge. Human evaluation requires…
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi +6
Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation…
Summarizing Community-based Question-Answer Pairs
Ting-Yao Hsu, Yoshi Suhara, Xiaolan Wang
Community-based Question Answering (CQA), which allows users to acquire their desired information, has increasingly become an essential component of online services in various doma…
SciCap: Generating Captions for Scientific Figures
Ting-Yao Hsu, C. Lee Giles, Ting-Hao 'Kenneth' Huang
Researchers use figures to communicate rich, complex information in scientific papers. The captions of these figures are critical to conveying effective messages. However, low-qual…
Visual Story Post-Editing
Ting-Yao Hsu, Chieh-Yang Huang, Yen-Chia Hsu +1
We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task. The dat…
On How Users Edit Computer-Generated Visual Stories
Ting-Yao Hsu, Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang
A significant body of research in Artificial Intelligence (AI) has focused on generating stories automatically, either based on prior story plots or input images. However, literatu…