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Visual Stereotypes and Language Generation: A Comparative Bias Study of MLLMs
Erfan Samieyan Sahneh • Jana Nikolovska
abstract
This project investigates the bias resilience of Multimodal Large Language Models (MLLMs) when interpreting visually ambiguous images, using a car crash dataset as the evaluation basis. By using carefully selected ambiguous images alongside a controlled set of neutral and biased prompts, we aim to assess both inherent biases from pretraining and susceptibility to suggestion-based bias. The findings will provide insights into the reliability, robustness, and ethical vulnerabilities of selected MLLMs in this context.
