  Recent advancements in Multimodal Large Language Models (MLLMs) have
significantly enhanced the comprehension of multimedia content, bringing
together diverse modalities such as text, images, and videos. However, a
critical challenge faced by these models, especially when processing video
inputs, is the occurrence of hallucinations - erroneous perceptions or
interpretations, particularly at the event level. This study introduces an
innovative method to address event-level hallucinations in MLLMs, focusing on
specific temporal understanding in video content. Our approach leverages a
novel framework that extracts and utilizes event-specific information from both
the event query and the provided video to refine MLLMs' response. We propose a
unique mechanism that decomposes on-demand event queries into iconic actions.
Subsequently, we employ models like CLIP and BLIP2 to predict specific
timestamps for event occurrences. Our evaluation, conducted using the
Charades-STA dataset, demonstrates a significant reduction in temporal
hallucinations and an improvement in the quality of event-related responses.
This research not only provides a new perspective in addressing a critical
limitation of MLLMs but also contributes a quantitatively measurable method for
evaluating MLLMs in the context of temporal-related questions.
