1 citations · 1 across the 11 of their papers we have counts for
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Helping Figures Tell their Story! Paper-Grounded Video Generation Explaining Complex Scientific Figures
Ishani Mondal, Javad Baghirov, Jordan Boyd-Graber
Scientific figures compress complex pipelines into a single canvas, yet understanding them requires paper-grounded, step-by-step narration aligned with visual highlights a capabili…
CANVAS: Continuity-Aware Narratives via Visual Agentic Storyboarding
Ishani Mondal, Yiwen Song, Mihir Parmar +4
Long-form visual storytelling requires maintaining continuity across shots, including consistent characters, stable environments, and smooth scene transitions. While existing gener…
SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity
Ishani Mondal, Meera Bharadwaj, Ayush Roy +2
We present SMART-Editor, a framework for compositional layout and content editing across structured (posters, websites) and unstructured (natural images) domains. Unlike prior mode…
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators
Feng Gu, Zongxia Li, Carlos Rafael Colon +3
Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annotators set the gold standards, hu…
SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement
Ishani Mondal, Zongxia Li, Yufang Hou +3
Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and a…
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness
Yoo Yeon Sung, Maharshi Gor, Eve Fleisig +2
Adversarial datasets should validate AI robustness by providing samples on which humans perform well, but models do not. However, as models evolve, datasets can become obsolete. Me…
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence
Zongxia Li, Ishani Mondal, Yijun Liang +2
Question answering (QA) can only make progress if we know if an answer is correct, but current answer correctness (AC) metrics struggle with verbose, free-form answers from large l…
How the Advent of Ubiquitous Large Language Models both Stymie and Turbocharge Dynamic Adversarial Question Generation
Yoo Yeon Sung, Ishani Mondal, Jordan Boyd-Graber
Dynamic adversarial question generation, where humans write examples to stump a model, aims to create examples that are realistic and informative. However, the advent of large lang…
CFMatch: Aligning Automated Answer Equivalence Evaluation with Expert Judgments For Open-Domain Question Answering
Zongxia Li, Ishani Mondal, Yijun Liang +2
Question answering (QA) can only make progress if we know if an answer is correct, but for many of the most challenging and interesting QA examples, current evaluation metrics to d…