VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

Understand

The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence.

• However, these advancements are accompanied by concerns about biased outputs, a challenge that has yet to be thoroughly explored.
• Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias.
• To address this problem, we introduce VLBiasBench, a comprehensive benchmark designed to evaluate biases in LVLMs.