Datasets:

bigai-nlco
/

VideoHallucer

Tasks:

Question Answering

Languages:

English

Size:

1K<n<10K

ArXiv:

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Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    DataFilesNotFoundError
Message:      No (supported) data files found in bigai-nlco/VideoHallucer
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 72, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1904, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1270, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 597, in infer_module_for_data_files
                  raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
              datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in bigai-nlco/VideoHallucer

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VideoHallucer

Paper: https://huggingface.co/papers/2406.16338

This work introduces VideoHallucer, the first comprehensive benchmark for hallucination detection in large video-language models (LVLMs). VideoHallucer categorizes hallucinations into two main types: intrinsic and extrinsic, offering further subcategories for detailed analysis, including object-relation, temporal, semantic detail, extrinsic factual, and extrinsic non-factual hallucinations. We adopt an adversarial binary VideoQA method for comprehensive evaluation, where pairs of basic and hallucinated questions are crafted strategically.

Data Statistics

	Object-Relation Hallucination	Temporal Hallucination	Semantic Detail Hallucination	External Factual Hallucination	External Nonfactual Hallucination
Questions	400	400	400	400	400
Videos	183	165	400	200	200

Evaluation

We provide VideoHallucerKit for evaluation

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