Datasets:

SNU-ISLAB
/

MVH-Bench

dataset_info:
  features:
    - name: group_id
      dtype: string
    - name: mvh_type
      dtype: string
    - name: category
      dtype: string
    - name: view_1
      dtype: image
    - name: view_2
      dtype: image
    - name: mc_questions
      list: string
    - name: mc_options
      list: string
    - name: mc_answers
      list: string
    - name: binary_questions
      list: string
    - name: binary_answers
      list: string
  splits:
    - name: validation
      num_bytes: 23895547
      num_examples: 80
    - name: test
      num_bytes: 196420891
      num_examples: 720
  download_size: 219687633
  dataset_size: 220316438
configs:
  - config_name: default
    data_files:
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - multimodal,
  - multi-view,
  - hallucination,
  - multi-image,
  - egocentric,
  - exocentric,
  - visual-question-answering
pretty_name: MVH-Bench

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

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 for MVH-Bench is supported in lmms-eval.

Example

python -m lmms_eval \
  --model qwen2_5_vl \
  --model_args pretrained=Qwen/Qwen2.5-VL-7B-Instruct \
  --tasks mvh_bench \
  --batch_size 1

Available Tasks

mvh_bench: Full benchmark on the test split, including both cross-instance and cross-view evaluations
mvh_bench_val: Validation split
mvh_bench_cross_instance: Cross-instance subset of the test split
mvh_bench_cross_view: Cross-view subset of the test split

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}
}