1 citations · 1 across the 5 of their papers we have counts for
6 papers
RepProbe: Can Video Language Models Count Repeated Actions? A Diagnostic Benchmark
Mahsa Khoshnoodi, Sarah Adel Bargal
Repetition counting requires identifying and enumerating action cycles over time. While state-of-the-art video language models describe actions fluently, they fail to reliably coun…
ChartProbe: A Diagnostic Study on Visual Reasoning through Perception, Grounding, and Simple Reasoning
Mahsa Khoshnoodi, Sarah Adel Bargal
Vision-language models (VLMs) remain unreliable on chart questions that require reasoning over visual quantities, and this weakness is usually attributed to a reasoning deficit and…
PROVE: Training-Free Prompt Recovery using Verifiable Evidence
Rupayan Mallick, Mahsa Khoshnoodi, Sarah Adel Bargal
Modern text-to-image models can generate highly realistic images from natural-language prompts, while recent advances in prompt inversion have made it increasingly feasible to reco…
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
Devichand Budagam, Ashutosh Kumar, Mahsa Khoshnoodi +3
Assessing the effectiveness of large language models (LLMs) in performing different tasks is crucial for understanding their strengths and weaknesses. This paper presents Hierarchi…
A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models
Mahsa Khoshnoodi, Vinija Jain, Mingye Gao +2
Despite the crucial importance of accelerating text generation in large language models (LLMs) for efficiently producing content, the sequential nature of this process often leads…
Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2)
Michael Saxon, Fatima Jahara, Mahsa Khoshnoodi +3
With advances in the quality of text-to-image (T2I) models has come interest in benchmarking their prompt faithfulness -- the semantic coherence of generated images to the prompts…