Datasets:

che111
/

OmniClean

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:

{
  "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`

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:

@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.