---
license: mit
task_categories:
- question-answering
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
tags:
- video
- hallucination
- benchmark
- video-language-models
configs:
  - config_name: external_factual
    data_files:
      - split: test
        path: external_factual/external_factual.json
  - config_name: external_nonfactual
    data_files:
      - split: test
        path: external_nonfactual/external_nonfactual.json
  - config_name: fact_detect
    data_files:
      - split: test
        path:
          - fact_detect/fact_detect.json
          - fact_detect/fact_detect_yn.json
  - config_name: object_relation
    data_files:
      - split: test
        path: object_relation/object_relation.json
  - config_name: semantic_detail
    data_files:
      - split: test
        path: semantic_detail/semantic_detail.json
  - config_name: temporal
    data_files:
      - split: test
        path: temporal/temporal.json
---

# VideoHallucer (mirror)

A redistribution of the **VideoHallucer** benchmark, bundled together with its
videos so the whole benchmark comes down in a single `snapshot_download`.

> This is **not** the official release. All credit goes to the original authors.
> Official code: https://github.com/patrick-tssn/VideoHallucer ·
> Official data: https://huggingface.co/datasets/bigai-nlco/VideoHallucer

## Dataset description

- **Repository:** [VideoHallucer](https://github.com/patrick-tssn/VideoHallucer)
- **Paper:** [2406.16338](https://arxiv.org/abs/2406.16338)
- **Leaderboard:** https://videohallucer.github.io/
- **Point of contact:** [Yuxuan Wang](mailto:wangyuxuan1@bigai.ai)

VideoHallucer is the first comprehensive benchmark for hallucination detection
in large video-language models (LVLMs). It categorizes hallucinations into
**intrinsic** and **extrinsic** types, with subcategories for object-relation,
temporal, semantic detail, external factual, and external non-factual
hallucination.

Evaluation is **adversarial binary VideoQA**: every item is a *pair* of yes/no
questions over the same video — a `basic` question whose answer is grounded in
the video, and a `hallucination` question crafted so a hallucinating model
answers it wrongly. A model is only credited when it gets **both** right, which
defeats the trivial always-yes / always-no strategies that binary QA otherwise
rewards.

## Data statistics

| | Object-Relation | Temporal | Semantic Detail | External Factual | External Non-factual |
| ---- | ---- | ---- | ---- | ---- | ---- |
| Questions | 400 | 400 | 400 | 400 | 400 |
| Videos | 183 | 165 | 400 | 200 | 200 |

## Contents

```
object_relation/     object_relation.json     + videos/
temporal/            temporal.json            + videos/
semantic_detail/     semantic_detail.json     + videos/
external_factual/    external_factual.json    + videos/
external_nonfactual/ external_nonfactual.json + videos/
fact_detect/         fact_detect.json, fact_detect_yn.json, modify.py + videos/
interaction/         interaction.json         + videos/
```

Each subset directory carries its own `videos/` folder, and the `video` field in
each JSON is a bare filename resolved against that sibling folder. The
`external_factual` and `external_nonfactual` subsets are posed over the same
video pool, so their `videos/` folders overlap.

`interaction/` is an extra subset present in the upstream data drop; it is not
part of the five headline categories above and is not exposed as a `datasets`
config.

### Record format

Every record pairs two questions over one video:

```json
{
  "basic": {
    "video": "1052_6143391925_916_970.mp4",
    "question": "Is there a baby in the video?",
    "answer": "yes"
  },
  "hallucination": {
    "video": "1052_6143391925_916_970.mp4",
    "question": "Is there a doll in the video?",
    "answer": "no"
  },
  "type": "subject"
}
```

## Usage

```python
from huggingface_hub import snapshot_download

root = snapshot_download(repo_id="shuzhig/VideoHallucer", repo_type="dataset")
```

A single subset:

```python
snapshot_download(
    repo_id="shuzhig/VideoHallucer",
    repo_type="dataset",
    allow_patterns="temporal/*",
)
```

Annotations only, no videos:

```python
snapshot_download(
    repo_id="shuzhig/VideoHallucer",
    repo_type="dataset",
    allow_patterns="*/*.json",
)
```

## Evaluation

Use the official
[VideoHallucerKit](https://github.com/patrick-tssn/VideoHallucer?tab=readme-ov-file#videohallucerkit).
The directory layout here matches what the kit expects, so pointing it at the
downloaded snapshot root is enough.

## Provenance and licensing

Mirrored from
[`bigai-nlco/VideoHallucer`](https://huggingface.co/datasets/bigai-nlco/VideoHallucer),
released under the **MIT license**. Source videos are drawn from existing public
video corpora and remain subject to their original terms. This mirror asserts no
additional rights. If you are an author and would like it removed, please open a
discussion.

## Citation

```bibtex
@article{wang2024videohallucer,
  title   = {VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models},
  author  = {Wang, Yuxuan and Wang, Yueqian and Zhao, Dongyan and Xie, Cihang and Zheng, Zilong},
  journal = {arXiv preprint arXiv:2406.16338},
  year    = {2024}
}
```