VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in
  Large Video-Language Models
Recent advancements in Multimodal Large Language Models (MLLMs) have extended
their capabilities to video understanding. Yet, these models are often plagued
by "hallucinations", where irrelevant or nonsensical content is generated,
deviating from the actual video context. 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. By evaluating
eleven LVLMs on VideoHallucer, we reveal that i) the majority of current models
exhibit significant issues with hallucinations; ii) while scaling datasets and
parameters improves models' ability to detect basic visual cues and
counterfactuals, it provides limited benefit for detecting extrinsic factual
hallucinations; iii) existing models are more adept at detecting facts than
identifying hallucinations. As a byproduct, these analyses further instruct the
development of our self-PEP framework, achieving an average of 5.38%
improvement in hallucination resistance across all model architectures.
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Yuxuan Wang
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Yuxuan Wang
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Yueqian Wang
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2024-06-25T08:14:29.905Z
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Yueqian Wang
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wangyueqian
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Dongyan Zhao
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Cihang Xie
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2024-06-25T08:14:51.184Z
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Cihang Xie
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cihangxie
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Zilong Zheng
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2024-06-25T02:46:08.771Z
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Zilong Zheng
user
zlzheng
VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in
  Large Video-Language Models
Recent advancements in Multimodal Large Language Models (MLLMs) have extended
their capabilities to video understanding. Yet, these models are often plagued
by "hallucinations", where irrelevant or nonsensical content is generated,
deviating from the actual video context. 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. By evaluating
eleven LVLMs on VideoHallucer, we reveal that i) the majority of current models
exhibit significant issues with hallucinations; ii) while scaling datasets and
parameters improves models' ability to detect basic visual cues and
counterfactuals, it provides limited benefit for detecting extrinsic factual
hallucinations; iii) existing models are more adept at detecting facts than
identifying hallucinations. As a byproduct, these analyses further instruct the
development of our self-PEP framework, achieving an average of 5.38%
improvement in hallucination resistance across all model architectures.
VideoHallucer, a comprehensive benchmark for detecting hallucinations in video-language models, reveals significant issues with current models and instructs the development of a self-PEP framework for improved hallucination resistance.
multimodal large language models MLLMs video understanding hallucinations VideoHallucer large video-language models LVLMs adversarial binary VideoQA object-relation temporal semantic detail extrinsic factual extrinsic non-factual hallucinations self-PEP framework