43 citations · 46 across the 9 of their papers we have counts for
11 papers
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…
Toward a social psychology of AI: language-model agents reproduce human-like minimal-group bias
Messi H. J. Lee
Language-model agents now interact in groups, but evaluations that probe memorised stereotype content or use models to simulate people leave this social behaviour unmeasured. We ad…
Language model agents show in-group trust bias invisible to standard behavioural audits
Messi H. J. Lee
Language-model agents are moving from single-user assistants into persistent networks that build trust and reputation with one another, and the same models increasingly control phy…
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…
Implicit Bias-Like Patterns in Reasoning Models
Messi H. J. Lee, Calvin K. Lai
Implicit biases refer to automatic mental processes that shape perceptions, judgments, and behaviors. Previous research on "implicit bias" in LLMs focused primarily on outputs rath…
Token-Level Entropy Reveals Demographic Disparities in Large Language Models
Messi H. J. Lee
A name alone measurably reshapes a language model's next-token distribution before a single token is sampled. We measure full-vocabulary Shannon entropy of the next-token distribut…
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…
Probability of Differentiation Reveals Brittleness of Homogeneity Bias in GPT-4
Messi H. J. Lee, Calvin K. Lai
Homogeneity bias in Large Language Models (LLMs) refers to their tendency to homogenize the representations of some groups compared to others. Previous studies documenting this bia…
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…
Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans
Messi H. J. Lee, Jacob M. Montgomery, Calvin K. Lai
Large language models (LLMs) are becoming pervasive in everyday life, yet their propensity to reproduce biases inherited from training data remains a pressing concern. Prior invest…