2 citations · 4 across the 21 of their papers we have counts for
23 papers
Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
Zongxia Li, Yucheng Shi, Zhongzhi Li +7
Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that may be unavailable during depl…
A Good Talk Does not Look Like a Summary, It Teaches You! Measuring Takeaways from Paper-to-Video Talks
Ishani Mondal, Aparna Garimella, Ananya Sai +2
Automatically generated videos from scientific papers are increasingly used for education and research dissemination. However, existing evaluation metrics mainly measure visual qua…
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…
AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?
Maharshi Gor, Yoo Yeon Sung, Yu Hou +4
AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration requires understanding when, w…
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…
Self-Rewarding Vision-Language Model via Reasoning Decomposition
Zongxia Li, Wenhao Yu, Chengsong Huang +8
Vision-Language Models (VLMs) often suffer from visual hallucinations: generating things that are not consistent with visual inputs and language shortcuts, where they skip the visu…
Semantically-Aware Rewards for Open-Ended R1 Training in Free-Form Generation
Zongxia Li, Yapei Chang, Yuhang Zhou +4
Evaluating open-ended long-form generation is challenging because it is hard to define what clearly separates good from bad outputs. Existing methods often miss key aspects like co…
VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Zongxia Li, Xiyang Wu, Guangyao Shi +6
Vision-Language Models (VLMs) have achieved strong results in video understanding, yet a key question remains: do they truly comprehend visual content or only learn shallow correla…
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…
GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration
Yoo Yeon Sung, Eve Fleisig, Yu Hou +2
Language models are often miscalibrated, leading to confidently incorrect answers. We introduce GRACE, a benchmark for language model calibration that incorporates comparison with…
NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization
Zheyuan Zhang, Runze Li, Tasnim Kabir +1
Image geo-localization is the task of predicting the specific location of an image and requires complex reasoning across visual, geographical, and cultural contexts. While prior Vi…
Large Language Models Struggle to Describe the Haystack without Human Help: Human-in-the-loop Evaluation of Topic Models
Zongxia Li, Lorena Calvo-Bartolomé, Alexander Hoyle +4
A common use of NLP is to facilitate the understanding of large document collections, with a shift from using traditional topic models to Large Language Models. Yet the effectivene…
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…
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
Xiyang Wu, Tianrui Guan, Dianqi Li +9
Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect r…
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
Zongxia Li, Andrew Mao, Daniel Stephens +5
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often us…
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…
Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines
Yoo Yeon Sung, Jordan Boyd-Graber, Naeemul Hassan
Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect f…
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Ishani Mondal, Michelle Yuan, Anandhavelu N +5
Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; howe…
Improving Question Answering with Generation of NQ-like Questions
Saptarashmi Bandyopadhyay, Shraman Pal, Hao Zou +2
Question Answering (QA) systems require a large amount of annotated data which is costly and time-consuming to gather. Converting datasets of existing QA benchmarks are challenging…