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Datasets:
che111
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OmniClean

Tasks:
Question Answering
Modalities:
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Formats:
json
Languages:
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Chinese
Size:
1K - 10K
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OmniClean
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---
pretty_name: OmniClean
language:
- en
- zh
multilinguality: multilingual
license: other
task_categories:
- question-answering
size_categories:
- 1K<n<10K
configs:
- config_name: slim
  data_files:
  - split: test
    path: omniclean.test.jsonl
---

# OmniClean

OmniClean is a leakage-aware omni-modal evaluation set built from retained examples across 9 source benchmarks. It is designed to reduce visual-shortcut effects in omni evaluation by applying visual-only probing where query-level filtering is defined, while keeping selected full subsets for protocol-exception benchmarks where a filtered subset is undefined or intentionally not reported.

This release contains **8,551** evaluation examples in a minimal `slim` JSONL format.

## What this release is

Raw omni benchmark scores can be inflated by visually answerable examples. OmniClean is intended to provide a cleaner evaluation target for audio-visual-language QA and related omni understanding tasks.

This release is for evaluation. It is not intended as a training corpus.

## Composition

Total examples: **8,551**

| Source benchmark (`dataset_source`) | Examples | Notes |
|---|---:|---|
| `AV_Odyssey_Bench` | 4555 | Full selected subset retained as a protocol exception |
| `VideoHolmes` | 885 | Query-level cleaned subset |
| `WorldSense` | 875 | Query-level cleaned subset |
| `IntentBench` | 660 | Query-level cleaned subset |
| `OmniBench` | 417 | Query-level cleaned subset |
| `CG-AV-Counting` | 376 | Full selected subset retained as a protocol exception |
| `OmniVideoBench` | 318 | Query-level cleaned subset |
| `Daily-Omni` | 237 | Query-level cleaned subset |
| `UNO-Bench` | 228 | Query-level cleaned subset |

## Data format

Each record contains the following fields:

- `dataset_source`: source benchmark name
- `source_id`: source sample identifier
- `question`: question text
- `options`: candidate answers; may be empty for some benchmarks
- `answer`: benchmark-native gold answer
- `media_paths`: relative media references with `image`, `audio`, and `video` lists
- `question_type`: benchmark-native question category; may be `null`

Example:

```json
{
  "dataset_source": "OmniVideoBench",
  "source_id": "omnivideobench:0",
  "question": "Before picking up the kitten, the blogger explains a sign. Which concepts can it be associated with?",
  "options": [
    "A.Ancient Chinese stories and Japanese anime",
    "B.Ancient Chinese Imperial Palace Architecture and Japanese Bar Names",
    "C.A certain type of Chinese cuisine and a certain type of Southeast Asian opera",
    "D.Chinese garden art and Western palace architecture"
  ],
  "answer": "Ancient Chinese stories and Japanese anime",
  "media_paths": {
    "image": [],
    "audio": [],
    "video": ["videos/video_1.mp4"]
  },
  "question_type": "reference reasoning"
}
```

## Important notes

### Benchmark-native answers
`answer` is not normalized into a single format across all sources. Depending on the benchmark, it may be:

- a single option letter such as `A`
- multiple option letters such as `D,E,F`
- a numeric answer such as `18`
- the full answer text
- a short free-form label such as `Yes`

Evaluation should therefore use benchmark-aware answer normalization.

### Optional fields by source benchmark
- `options` can be empty for some examples.
- `question_type` can be `null` for some examples.
- `media_paths` always contains the keys `image`, `audio`, and `video`, but some lists are empty.

### Protocol exceptions
Two source benchmarks are intentionally retained as selected full subsets in this release:

- `AV_Odyssey_Bench`: a visual-only filtered subset is not defined because some answer options contain audio-bearing content.
- `CG-AV-Counting`: visual-only probing is used diagnostically, but a filtered-score benchmark is not reported because further exclusion would overly shrink an already difficult subset.

## Loading with `datasets`

```python
from datasets import load_dataset

ds = load_dataset("che111/OmniClean", "slim", split="test")
print(ds[0])
```

## Limitations

- This release keeps benchmark-native answer formats instead of forcing a single unified answer schema.
- Source benchmarks differ in modality structure: some examples are video-only, some are image+audio, and some are audio+video.
- Relative paths in `media_paths` should be interpreted with respect to the released data layout.

## Citation

If you use OmniClean, please cite the accompanying paper:

```bibtex
@misc{liu2026boostingomnimodallanguagemodels,
      title={Boosting Omni-Modal Language Models: Staged Post-Training with Visually Debiased Evaluation}, 
      author={Che Liu and Lichao Ma and Xiangyu Tony Zhang and Yuxin Zhang and Haoyang Zhang and Xuerui Yang and Fei Tian},
      year={2026},
      eprint={2605.12034},
      archivePrefix={arXiv},
      primaryClass={cs.MM},
      url={https://arxiv.org/abs/2605.12034}, 
}
```

## License

Please replace this section with the final license and confirm that redistribution terms are compatible with all included source benchmarks and media assets.