When Vision-Language Models Judge Without Seeing: Exposing Informativeness Bias
The reliability of VLM-as-a-Judge is critical for the automatic evaluation of vision-language models (VLMs). Despite recent progress, our analysis reveals that VLM-as-a-Judge often pays limited attention to the image when making decisions. Instead, they often blindly favor the more informative answer, even when they can recognize it conflicts with the image content. We call this problem informativeness bias, which significantly undermines judge reliability. To address it, we propose BIRCH (Balanced Informativeness and CoRrectness with a Truthful AnCHor), a judging paradigm that first corrects inconsistencies with the image content in candidate answers, and then compares the answers agains...
| Authors | Xiaohan Zou, Roshan Sridhar, Mohammadtaher Safarzadeh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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