ELV-Halluc: Benchmarking Semantic Aggregation Hallucinations in Long
  Video Understanding
Authors: name: Hao Lu, name: Jiahao Wang, name: Yaolun Zhang, name: Ruohui Wang, name: Xuanyu Zheng, name: Yepeng Tang, name: Dahua Lin, name: Lewei Lu

Video multimodal large language models (Video-MLLMs) have achieved remarkable
progress in video understanding. However, they remain vulnerable to
hallucination-producing content inconsistent with or unrelated to video inputs.
Previous video hallucination benchmarks primarily focus on short-videos. They
attribute hallucinations to factors such as strong language priors, missing
frames, or vision-language biases introduced by the visual encoder. While these
causes indeed account for most hallucinations in short videos, they still
oversimplify the cause of hallucinations. Sometimes, models generate incorrect
outputs but with correct frame-level semantics. We refer to this type of
hallucination as Semantic Aggregation Hallucination (SAH), which arises during
the process of aggregating frame-level semantics into event-level semantic
groups. Given that SAH becomes particularly critical in long videos due to
increased semantic complexity across multiple events, it is essential to
separate and thoroughly investigate the causes of this type of hallucination.
To address the above issues, we introduce ELV-Halluc, the first benchmark
dedicated to long-video hallucination, enabling a systematic investigation of
SAH. Our experiments confirm the existence of SAH and show that it increases
with semantic complexity. Additionally, we find that models are more prone to
SAH on rapidly changing semantics. Moreover, we discuss potential approaches to
mitigate SAH. We demonstrate that positional encoding strategy contributes to
alleviating SAH, and further adopt DPO strategy to enhance the model's ability
to distinguish semantics within and across events. To support this, we curate a
dataset of 8K adversarial data pairs and achieve improvements on both
ELV-Halluc and Video-MME, including a substantial 27.7% reduction in SAH ratio.