TL;DR
This survey analyzes hallucinations in Video Large Language Models, categorizing them into dynamic distortion and content fabrication, and reviews evaluation, mitigation strategies, and future research directions.
Contribution
It introduces a systematic taxonomy of hallucinations in Vid-LLMs and consolidates recent advances in evaluation and mitigation techniques.
Findings
Hallucinations are categorized into dynamic distortion and content fabrication.
Root causes include limited temporal representation and visual grounding.
Promising future directions involve motion-aware encoders and counterfactual learning.
Abstract
Despite significant progress in video-language modeling, hallucinations remain a persistent challenge in Video Large Language Models (Vid-LLMs), referring to outputs that appear plausible yet contradict the content of the input video. This survey presents a comprehensive analysis of hallucinations in Vid-LLMs and introduces a systematic taxonomy that categorizes them into two core types: dynamic distortion and content fabrication, each comprising two subtypes with representative cases. Building on this taxonomy, we review recent advances in the evaluation and mitigation of hallucinations, covering key benchmarks, metrics, and intervention strategies. We further analyze the root causes of dynamic distortion and content fabrication, which often result from limited capacity for temporal representation and insufficient visual grounding. These insights inform several promising directions for…
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