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.

通过提取和利用事件查询和提供的视频中的事件特定信息，我们介绍了一种创新的方法来解决多模态大型语言模型中发生的事件级幻觉问题，重点关注视频内容中的时间理解问题，在Charades-STA数据集上的评估表明，该研究不仅在解决多模态大型语言模型的关键限制方面提供了新的视角，还为在与时间相关的问题上评估多模态大型语言模型提供了一种定量可测量的方法。

时间洞察增强：减轻多模态大语言模型中的时间幻觉