MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs

Understand

Spurious bias, a tendency to exploit spurious correlations between superficial input attributes and prediction targets, has revealed a severe robustness pitfall in classical machine learning problems.

• Multimodal Large Language Models (MLLMs), which leverage pretrained vision and language models, have recently demonstrated strong capability in joint vision-language understanding.
• However, both the presence and severity of spurious biases in MLLMs remain poorly understood.
• In this work, we address this gap by analyzing the spurious biases in the multimodal setting and uncovering the specific inference-time data patterns that can manifest this problem.