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

• URL: http://arxiv.org/abs/2407.02730v1
• Date: Wed, 3 Jul 2024 00:59:03 GMT
• Title: MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context
• Authors: Zishan Gu, Changchang Yin, Fenglin Liu, Ping Zhang,
• Abstract summary: Large Vision Language Models (LVLMs) have recently achieved superior performance in various tasks on natural image and text data. Despite their advancements, there has been scant research on the robustness of these models against hallucination when fine-tuned on smaller datasets. We introduce a new benchmark dataset, the Medical Visual Hallucination Test (MedVH), to evaluate the hallucination of domain-specific LVLMs.
• Score: 21.562034852024272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Test (MedVH), to evaluate the hallucination of domain-specific LVLMs. MedVH comprises five tasks to evaluate hallucinations in LVLMs within the medical context, which includes tasks for comprehensive understanding of textual and visual input, as well as long textual response generation. Our extensive experiments with both general and medical LVLMs reveal that, although medical LVLMs demonstrate promising performance on standard medical tasks, they are particularly susceptible to hallucinations, often more so than the general models, raising significant concerns about the reliability of these domain-specific models. For medical LVLMs to be truly valuable in real-world applications, they must not only accurately integrate medical knowledge but also maintain robust reasoning abilities to prevent hallucination. Our work paves the way for future evaluations of these studies.

Related papers

• HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language Models [57.58426038241812]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in performing complex multimodal tasks. We propose HALLUCINOGEN, a novel visual question answering (VQA) object hallucination attack benchmark. We extend our benchmark to high-stakes medical applications and introduce MED-HALLUCINOGEN, hallucination attacks tailored to the biomedical domain.
  arXiv  Detail & Related papers  (2024-12-29T23:56:01Z)
• Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning [151.4060202671114]
  multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing vision-language tasks. This paper introduces a novel bottom-up reasoning framework to address hallucinations in MLLMs. Our framework systematically addresses potential issues in both visual and textual inputs by verifying and integrating perception-level information with cognition-level commonsense knowledge.
  arXiv  Detail & Related papers  (2024-12-15T09:10:46Z)
• A Survey of Hallucination in Large Visual Language Models [48.794850395309076]
  The existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. The structure of LVLMs and main causes of hallucination generation are introduced. The available hallucination evaluation benchmarks for LVLMs are presented.
  arXiv  Detail & Related papers  (2024-10-20T10:58:58Z)
• Detecting and Evaluating Medical Hallucinations in Large Vision Language Models [22.30139330566514]
  Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications. LVLMs inherit susceptibility to hallucinations-a significant concern in high-stakes medical contexts. We introduce Med-HallMark, the first benchmark specifically designed for hallucination detection and evaluation. We also present MediHallDetector, a novel Medical LVLM engineered for precise hallucination detection.
  arXiv  Detail & Related papers  (2024-06-14T17:14:22Z)
• OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM [48.16696073640864]
  We introduce OmniMedVQA, a novel comprehensive medical Visual Question Answering (VQA) benchmark. All images in this benchmark are sourced from authentic medical scenarios. We have found that existing LVLMs struggle to address these medical VQA problems effectively.
  arXiv  Detail & Related papers  (2024-02-14T13:51:56Z)
• Hallucination Benchmark in Medical Visual Question Answering [2.4302611783073145]
  We created a hallucination benchmark of medical images paired with question-answer sets and conducted a comprehensive evaluation of the state-of-the-art models. The study provides an in-depth analysis of current models' limitations and reveals the effectiveness of various prompting strategies.
  arXiv  Detail & Related papers  (2024-01-11T10:52:17Z)
• Evaluation and Analysis of Hallucination in Large Vision-Language Models [49.19829480199372]
  Large Vision-Language Models (LVLMs) have recently achieved remarkable success. LVLMs are still plagued by the hallucination problem. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input.
  arXiv  Detail & Related papers  (2023-08-29T08:51:24Z)
• Med-HALT: Medical Domain Hallucination Test for Large Language Models [0.0]
  This research paper focuses on the challenges posed by hallucinations in large language models (LLMs) We propose a new benchmark and dataset, Med-HALT (Medical Domain Hallucination Test), designed specifically to evaluate and reduce hallucinations.
  arXiv  Detail & Related papers  (2023-07-28T06:43:04Z)
• Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
  This work presents the first systematic study on object hallucination of large vision-language models (LVLMs) We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions. We propose a polling-based query method called POPE to evaluate the object hallucination.
  arXiv  Detail & Related papers  (2023-05-17T16:34:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.