MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Large Vision Language Models (LVLMs) have recently achieved superior performance in various tasks on natural image and text data, which inspires a large amount of studies for LVLMs fine-tuning and training. Despite their advancements, there has been scant research on the robustness of these models against hallucination when fine-tuned on smaller datasets. In this study, we introduce a new benchmark dataset, the Medical Visual Hallucination evaluation benchmark (MedVH), to evaluate the hallucination of domain-specific LVLMs. MedVH comprises six tasks to evaluate hallucinations in LVLMs within the medical context, which includes tasks for a comprehensive understanding of textual and visual input, as well as long textual response generation.