SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models

Peter Carragher, Nikitha Rao, Abhinand Jha, Raghav R, Kathleen M. Carley

Abstract:

Vision language models (VLM) demonstrate sophisticated
multimodal reasoning yet are prone to hallucination when confronted with knowledge conflicts, impeding their deployment
in information-sensitive contexts. While existing research addresses robustness in unimodal models, the multimodal domain lacks systematic investigation of cross-modal knowledge
conflicts. This research introduces SEGSUB, a framework for
applying targeted image perturbations to investigate VLM resilience against knowledge conflicts. Our analysis reveals distinct vulnerability patterns: while VLMs are robust to parametric conflicts (∼ 20% adherence rates), they exhibit significant
weaknesses in identifying counterfactual conditions (< 30%accuracy) and resolving source conflicts (< 1% accuracy).
Correlations between contextual richness and hallucination
rate (r = -0.368, p = 0.003) reveal the kinds of images that are
likely to cause hallucinations. Through targeted fine-tuning
on our benchmark dataset, we demonstrate improvements in
VLM knowledge conflict detection, establishing a foundation
for developing hallucination-resilient multimodal systems in
information-sensitive environments.