The Curse of Multi-Modalities: Evaluating Hallucinations of Large
  Multimodal Models across Language, Visual, and Audio
Recent advancements in large multimodal models (LMMs) have significantly
enhanced performance across diverse tasks, with ongoing efforts to further
integrate additional modalities such as video and audio. However, most existing
LMMs remain vulnerable to hallucinations, the discrepancy between the factual
multimodal input and the generated textual output, which has limited their
applicability in various real-world scenarios. This paper presents the first
systematic investigation of hallucinations in LMMs involving the three most
common modalities: language, visual, and audio. Our study reveals two key
contributors to hallucinations: overreliance on unimodal priors and spurious
inter-modality correlations. To address these challenges, we introduce the
benchmark The Curse of Multi-Modalities (CMM), which comprehensively evaluates
hallucinations in LMMs, providing a detailed analysis of their underlying
issues. Our findings highlight key vulnerabilities, including imbalances in
modality integration and biases from training data, underscoring the need for
balanced cross-modal learning and enhanced hallucination mitigation strategies.
Based on our observations and findings, we suggest potential research
directions that could enhance the reliability of LMMs.
6710899cb95dec8a173c7140
Sicong Leng
6710899cb95dec8a173c7141
Yun Xing
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2024-10-22T08:02:00.241Z
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Xing Yun
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xing0047
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Zesen Cheng
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2024-10-17T11:24:30.926Z
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Zesen Cheng
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ClownRat
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Yang Zhou
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2024-11-03T14:24:03.059Z
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ZHOU Yang
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youngzhou12
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Hang Zhang
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Xin Li
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2024-10-18T08:27:27.753Z
63913b120cf6b11c487ca31d
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Xin Li
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lixin4ever
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Deli Zhao
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Shijian Lu
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Chunyan Miao
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Lidong Bing
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2024-10-25T09:31:15.975Z
6454685a548f22be598414c4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
Lidong Bing
user
LidongBing
The Curse of Multi-Modalities: Evaluating Hallucinations of Large
  Multimodal Models across Language, Visual, and Audio
Recent advancements in large multimodal models (LMMs) have significantly
enhanced performance across diverse tasks, with ongoing efforts to further
integrate additional modalities such as video and audio. However, most existing
LMMs remain vulnerable to hallucinations, the discrepancy between the factual
multimodal input and the generated textual output, which has limited their
applicability in various real-world scenarios. This paper presents the first
systematic investigation of hallucinations in LMMs involving the three most
common modalities: language, visual, and audio. Our study reveals two key
contributors to hallucinations: overreliance on unimodal priors and spurious
inter-modality correlations. To address these challenges, we introduce the
benchmark The Curse of Multi-Modalities (CMM), which comprehensively evaluates
hallucinations in LMMs, providing a detailed analysis of their underlying
issues. Our findings highlight key vulnerabilities, including imbalances in
modality integration and biases from training data, underscoring the need for
balanced cross-modal learning and enhanced hallucination mitigation strategies.
Based on our observations and findings, we suggest potential research
directions that could enhance the reliability of LMMs.
Investigation into hallucinations in large multimodal models highlights vulnerabilities in modality integration and suggests strategies for improved reliability.
large multimodal models LMMs hallucinations unimodal priors spurious inter-modality correlations benchmark Curse of Multi-Modalities CMM modality integration cross-modal learning hallucination mitigation