ViLBias: Detecting and Reasoning about Bias in Multimodal Content

Understand

Detecting bias in multimodal news requires models that reason over text--image pairs, not just classify text.

• In response, we present ViLBias, a VQA-style benchmark and framework for detecting and reasoning about bias in multimodal news.
• The dataset comprises 40,945 text--image pairs from diverse outlets, each annotated with a bias label and concise rationale using a two-stage LLM-as-annotator pipeline with hierarchical majority voting and human-in-the-loop validation.
• We evaluate Small Language Models (SLMs), Large Language Models (LLMs), and Vision--Language Models (VLMs) across closed-ended classification and open-ended reasoning (oVQA), and compare parameter-efficient tuning strategies.