4 papers
What's Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning
Zhaotian Weng, Haoxuan Li, Xin Eric Wang +2
Despite the impressive performance of vision-language models (VLMs) on downstream tasks, their ability to understand and reason about causal relationships in visual inputs remains…
Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective
Zhaotian Weng, Zijun Gao, Jerone Andrews +1
Vision-language models (VLMs) pre-trained on extensive datasets can inadvertently learn biases by correlating gender information with specific objects or scenarios. Current methods…
BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agents
Haoxuan Li, Mingyu Derek Ma, Jen-tse Huang +3
Detecting biases in structured data is a complex and time-consuming task. Existing automated techniques are limited in diversity of data types and heavily reliant on human case-by-…
CLIMB: A Benchmark of Clinical Bias in Large Language Models
Yubo Zhang, Shudi Hou, Mingyu Derek Ma +3
Large language models (LLMs) are increasingly applied to clinical decision-making. However, their potential to exhibit bias poses significant risks to clinical equity. Currently, t…