Distorted or Fabricated? A Survey on Hallucination in Video LLMs

Researchers have conducted a comprehensive survey on hallucinations in Video Large Language Models (Vid-LLMs), identifying two core types—dynamic distortion and content fabrication—and their root causes in temporal representation limitations and insufficient visual grounding. The study reviews evaluation benchmarks, mitigation strategies, and proposes future directions including motion-aware encoders and counterfactual learning to improve reliability.

Video Large Language Models represent a significant frontier in multimodal AI, combining visual understanding with language generation. However, the persistent problem of hallucinations—where models generate plausible-sounding but factually incorrect outputs—undermines their reliability for critical applications. This comprehensive survey addresses a fundamental challenge that has hindered broader adoption of Vid-LLM technology across industries requiring high accuracy and trustworthiness.

The research emerges from growing recognition that existing video-language models struggle with temporal coherence and visual grounding. Previous work identified isolated instances of hallucinations, but this survey systematically categorizes them into dynamic distortion (temporal inconsistencies within video sequences) and content fabrication (completely invented details absent from source material). Understanding these distinctions enables more targeted intervention strategies rather than generic improvement approaches.

The implications extend across multiple sectors. In healthcare, surveillance, education, and content creation, Vid-LLM accuracy directly impacts decision-making and safety. The identified root causes—limited temporal representation capacity and insufficient visual grounding mechanisms—suggest that current architectural approaches have fundamental limitations. Organizations deploying these systems must account for hallucination risks in their pipelines.

The proposed solutions of motion-aware visual encoders and counterfactual learning represent promising but still-developmental approaches. Motion-aware encoders could better capture video dynamics that current models miss, while counterfactual learning might strengthen models' ability to distinguish actual content from plausible fabrications. The field should expect incremental progress rather than immediate breakthroughs, with practical reliability likely improving over 18-24 months as these techniques mature.

• →Hallucinations in Vid-LLMs fall into two categories: dynamic distortion and content fabrication, each with distinct causes and mitigation strategies.
• →Root causes stem from limited temporal representation capacity and insufficient visual grounding in current model architectures.
• →Motion-aware visual encoders and counterfactual learning show promise as intervention strategies for reducing hallucinations.
• →Comprehensive evaluation benchmarks and metrics are now available to systematically assess and track hallucination improvements.
• →Reliable video-language systems remain critical for deployment in high-stakes applications requiring trustworthy AI.