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20222026
most citedImproving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis

2 citations · 4 across the 14 of their papers we have counts for

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17 papers · 1 filter

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL20251 cited

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…

cs.CL2025

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…