arXiv:2602.01740v3 Announce Type: replace-cross Abstract: Video language models (Video-LLMs) are prone to hallucinations, generating plausible but ungrounded content when visual evidence is weak, ambiguous, or biased. Existing methods, such as contrastive decoding (CD), rely on random perturbations to construct contrastive data for hallucination mitigation, but often fail to target the visual cues that drive hallucination or align with model weaknesses. We propose Model-Aware Counterfactual Data based Contrastive Decoding (MACD), an inference strategy that combines model-guided counterfactual
The proliferation of Video-LLMs has amplified concerns around AI hallucination, making real-time mitigation techniques increasingly critical for reliable deployment.
This development addresses a core limitation of current video language models, potentially making them more trustworthy and applicable in sensitive domains.
The ability to more effectively reduce hallucinations via model-aware counterfactual data generation marks an improvement over previous random perturbation methods.
- · AI developers
- · Video content analysis platforms
- · Autonomous systems
- · Generative AI applications
- · Applications reliant on unmitigated Video-LLBs
- · Relying solely on external validation for AI outputs
Increased reliability and adoption of video-language models in enterprise and critical applications.
Accelerated development of AI agents that depend on visual understanding and contextual reasoning.
New ethical frameworks and regulatory standards emerging to define 'acceptable' levels of AI hallucination.
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Read at arXiv cs.LG