Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

• URL: http://arxiv.org/abs/2406.10185v1
• Date: Fri, 14 Jun 2024 17:14:22 GMT
• Title: Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
• Authors: Jiawei Chen, Dingkang Yang, Tong Wu, Yue Jiang, Xiaolu Hou, Mingcheng Li, Shunli Wang, Dongling Xiao, Ke Li, Lihua Zhang,
• Abstract summary: 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.
• Score: 22.30139330566514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications, including medical visual question answering and imaging report generation. While these models inherit the robust capabilities of foundational Large Language Models (LLMs), they also inherit susceptibility to hallucinations-a significant concern in high-stakes medical contexts where the margin for error is minimal. However, currently, there are no dedicated methods or benchmarks for hallucination detection and evaluation in the medical field. To bridge this gap, we introduce Med-HallMark, the first benchmark specifically designed for hallucination detection and evaluation within the medical multimodal domain. This benchmark provides multi-tasking hallucination support, multifaceted hallucination data, and hierarchical hallucination categorization. Furthermore, we propose the MediHall Score, a new medical evaluative metric designed to assess LVLMs' hallucinations through a hierarchical scoring system that considers the severity and type of hallucination, thereby enabling a granular assessment of potential clinical impacts. We also present MediHallDetector, a novel Medical LVLM engineered for precise hallucination detection, which employs multitask training for hallucination detection. Through extensive experimental evaluations, we establish baselines for popular LVLMs using our benchmark. The findings indicate that MediHall Score provides a more nuanced understanding of hallucination impacts compared to traditional metrics and demonstrate the enhanced performance of MediHallDetector. We hope this work can significantly improve the reliability of LVLMs in medical applications. All resources of this work will be released soon.

Related papers

• MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models [82.30696225661615]
  We introduce MedHallu, the first benchmark specifically designed for medical hallucination detection. We show that state-of-the-art LLMs, including GPT-4o, Llama-3.1, and medically fine-tuned UltraMedical, struggle with this binary hallucination detection task. Using bidirectional entailment clustering, we show that harder-to-detect hallucinations are semantically closer to ground truth.
  arXiv  Detail & Related papers  (2025-02-20T06:33:23Z)
• 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)
• MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models [0.0]
  Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications. Their propensity for hallucinations presents substantial risks to patient care. This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations in MLLMs.
  arXiv  Detail & Related papers  (2024-12-25T16:51:29Z)
• 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)
• Hallu-PI: Evaluating Hallucination in Multi-modal Large Language Models within Perturbed Inputs [54.50483041708911]
  Hallu-PI is the first benchmark designed to evaluate hallucination in MLLMs within Perturbed Inputs. Hallu-PI consists of seven perturbed scenarios, containing 1,260 perturbed images from 11 object types. Our research reveals a severe bias in MLLMs' ability to handle different types of hallucinations.
  arXiv  Detail & Related papers  (2024-08-02T16:07:15Z)
• MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context [21.562034852024272]
  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.
  arXiv  Detail & Related papers  (2024-07-03T00:59:03Z)
• HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination Evaluation [19.318217051269382]
  Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP) HalluDial is the first comprehensive large-scale benchmark for automatic dialogue-level hallucination evaluation. The benchmark includes 4,094 dialogues with a total of 146,856 samples.
  arXiv  Detail & Related papers  (2024-06-11T08:56:18Z)
• 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)
• 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.