MVH-Bench

MVH-Bench is the benchmark introduced in:

Revealing Multi-View Hallucination in Large Vision-Language Models
Wooje Park^*, Insu Lee^*, Soohyun Kim, Jaeyun Jang, Minyoung Noh, Kyuhong Shim, and Byonghyo Shim (* Equal contribution)
EMNLP 2026 Main

Paper

MVH-Bench is a multi-view visual question answering benchmark designed to evaluate Multi-View Hallucination (MVH) in Large Vision-Language Models (LVLMs).

The dataset contains 4,800 question-answer pairs organized into 800 image-pair groups, with each group containing two multiple-choice questions and four binary questions.

Split	Image-Pair Groups	MC QA	Binary QA	Total QA
Validation	80	160	320	480
Test	720	1,440	2,880	4,320
Total	800	1,600	3,200	4,800

Categories

MVH-Bench contains two types of multi-view hallucination:

• cross_view: hallucination caused by visual evidence from an incorrect viewpoint
• cross_instance: hallucination caused by visual evidence from an incorrect instance

The benchmark contains four evaluation categories:

• Action
• Object
• Numerical
• Spatial

Each image-pair group contains two question formats:

• Multiple Choice (MC)
• Binary (Yes/No)

Dataset Structure

Each example corresponds to one image-pair group and contains the following fields:

• group_id: unique group identifier
• mvh_type: MVH type (cross_view or cross_instance)
• category: question category
• view_1: first image in the multi-view pair
• view_2: second image in the multi-view pair
• mc_questions: two multiple-choice questions
• mc_options: three answer choices shared by the two MC questions
• mc_answers: ground-truth answers for the two MC questions
• binary_questions: four binary questions
• binary_answers: ground-truth answers for the four binary questions

Usage

from datasets import load_dataset

dataset = load_dataset(
    "SNU-ISLAB/MVH-Bench",
    split="test",
)

sample = dataset[0]

print(sample["mvh_type"])
print(sample["category"])

print(sample["mc_questions"])
print(sample["mc_options"])
print(sample["mc_answers"])

print(sample["binary_questions"])
print(sample["binary_answers"])

view_1 = sample["view_1"]
view_2 = sample["view_2"]

The validation split can be loaded with:

validation = load_dataset(
    "SNU-ISLAB/MVH-Bench",
    split="validation",
)

Evaluation

For multiple-choice questions, we report:

• Accuracy (Acc): accuracy over individual MC questions
• Pair Accuracy (p-Acc): the proportion of image-pair groups in which both MC questions are answered correctly
• Adversarial Error Rate (AER): among incorrect MC predictions, the proportion that select the adversarial alternative rather than the Neither option

For binary questions, we report:

• Accuracy (Acc): accuracy over individual Yes/No questions
• Pair Accuracy (p-Acc): the proportion of positive-negative question pairs in which both questions are answered correctly
• Quad Accuracy (q-Acc): the proportion of image-pair groups in which all four binary questions are answered correctly
• Yes Error Rate (YER): the proportion of erroneous binary predictions biased toward Yes

The overall MVH-Score jointly measures performance on the multiple-choice and binary evaluations:

MVH-Score = Binary Acc + Binary p-Acc + Binary q-Acc + MC Acc + MC p-Acc

AER and YER are reported as diagnostic error metrics and are not included in the MVH-Score.

Standardized evaluation support for lmms-eval will be provided.

Citation

If you use MVH-Bench in your research, please cite:

@article{park2026revealing,
  title={Revealing Multi-View Hallucination in Large Vision-Language Models},
  author={Park, Wooje and Lee, Insu and Kim, Soohyun and Jang, Jaeyun and Noh, Minyoung and Shim, Kyuhong and Shim, Byonghyo},
  journal={arXiv preprint arXiv:2603.23934},
  year={2026}
}