MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context
Authors: Zishan Gu, Changchang Yin, Fenglin Liu, Ping Zhang


Reviewer paper summaries: This study introduces the Medical Visual Hallucination Test (MedVH), a benchmark dataset to evaluate hallucinations in domain-specific LVLMs. MedVH comprises five tasks to assess hallucinations in medical contexts, including understanding textual and visual inputs and generating long textual responses. Experiments with both general and medical LVLMs reveal that medical LVLMs show that though medical LVLMs perform well on standard tasks, are more prone to hallucinations than general models, which raises concerns about their reliability in real-world applications.
Summary of strengths: 1. This work focuses on investigating hallucinations of LVLMs in novel, domain-specific tasks for medical applications, while there are not many prior works have been released in this domain. The findings and experiments provided in this paper help to raise the reliability concerns in applying LVLMs in medical fields. 
2. The figure that provides the details of each task is clear and covers all tasks included in this paper and the experiments evaluating the performance of general-purpose and domain-specific LVLMs are sufficient and comprehensive.
Summary of weaknesses: 1. The literature review section (Section 2) is not sufficient. More related works on sub-domain tasks, datasets, and previous works on hallucinations are needed.
2. More hallucination cases are needed. Meanwhile, the authors need to highlight the contradictions between the ground truth and the wrong context in those hallucination cases, (for example, in Fig. 2), like the correct answer or medical facts, or the region that each question is referring to, for those non-medical readers to understand.
3. Section 3.5 needs to be re-organized as different tasks may use different metrics and the authors need to mention metrics used in each task instead of mixing all metrics included in this dataset in a single section.
4. A discussion sub-section is expected to conclude the findings in the experiment section to make it easier for readers to understand the contribution of this work.
Comments, suggestions and typos: 1. As a paper presenting a novel benchmark, it might be better to present the data sources (Fig. 7) and distributions (Fig. 8) in the main paper.
2. More hallucination cases are needed.
Overall assessment scores: 3