43 citations · 46 across the 9 of their papers we have counts for
5 papers · 1 filter
Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation
Yumi Lee, Harim Oh, Hyoryung Kim +52
The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pathology report generation remai…
Visual Cues of Gender and Race are Associated with Stereotyping in Vision-Language Models
Messi H. J. Lee, Soyeon Jeon, Jacob M. Montgomery +1
Current research on bias in Vision Language Models (VLMs) has important limitations: it is focused exclusively on trait associations while ignoring other forms of stereotyping, it…
How Robust Is Homogeneity Bias in LLMs? Evidence Across Models, Decoding Settings, and Identity Signals
Messi H. J. Lee
Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias gene…
Vision-Language Models Generate More Homogeneous Stories for Phenotypically Black Individuals
Messi H. J. Lee, Soyeon Jeon
Vision-Language Models (VLMs) extend Large Language Models' capabilities by integrating image processing, but concerns persist about their potential to reproduce and amplify human…
More Distinctively Black and Feminine Faces Lead to Increased Stereotyping in Vision-Language Models
Messi H. J. Lee, Jacob M. Montgomery, Calvin K. Lai
Vision Language Models (VLMs), exemplified by GPT-4V, adeptly integrate text and vision modalities. This integration enhances Large Language Models' ability to mimic human percepti…