SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models
This addresses the issue of hallucination in VLMs for information-sensitive applications, representing an incremental advance by systematically investigating cross-modal knowledge conflicts.
The paper tackled the problem of vision-language models (VLMs) being prone to hallucination under knowledge conflicts, introducing the SegSub framework to evaluate their robustness. The results showed VLMs are robust to parametric conflicts (20% adherence rates) but weak in counterfactual conditions (<30% accuracy) and source conflicts (<1% accuracy), with fine-tuning improving detection.
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.