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

TL;DR

This paper introduces , a framework for testing vision-language models' robustness against knowledge conflicts and hallucinations through image perturbations, revealing vulnerabilities and improving detection via targeted fine-tuning.

Contribution

The study presents a systematic framework for evaluating and enhancing VLM robustness to knowledge conflicts and hallucinations, filling a gap in multimodal model research.

Findings

VLMs are robust to parametric conflicts (~20% adherence)

VLMs struggle with counterfactual identification (<30% accuracy)

Fine-tuning improves conflict detection capabilities

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.