DMC-CF: 用于因果推理的动态多模态反事实QA基准
DMC-CF: Dynamic Multimodal CounterFactual QA benchmark for Causal Reasoning
AI总结：
针对现有因果推理数据集规模有限或基于非真实数据的问题，提出基于真实视频的大规模多模态因果反事实推理基准DMC-CF-Static，并利用动态图干预框架构建动态评估基准DMC-CF-Dynamic，实验表明当前多模态大模型在真实场景下的因果推理能力仍需大幅提升。
摘要：
随着多模态大语言模型（MLLMs）的快速发展，模型已展现出日益强大的多模态能力。然而，通过统计学习训练的MLLMs能否真正理解现实世界背后的因果关系仍是一个关键研究问题。近年来，众多多模态因果推理数据集被提出，但这些数据集要么规模有限，要么基于合成图像和视频、卡通内容或其他非真实多模态来源构建。为解决这些局限性，我们收集真实世界视频并构建了DMC-CF-Static，一个大规模多模态因果反事实推理基准。此外，为缓解传统静态评估中的数据污染等问题，我们使用因果图表示因果事件，并提出动态图干预（DGI）框架，从DMC-CF-Static构建动态评估基准DMC-CF-Dynamic。在包含静态和动态评估基准的整体DMC-CF上的实验结果表明，当前多模态大语言模型在真实场景下的多模态因果推理能力仍需大幅提升。
英文摘要：
With the rapid advancement of multimodal large language models (MLLMs), models have demonstrated increasingly powerful multimodal capabilities. However, whether MLLMs trained through statistical learning can truly understand the causal relationships underlying the real world remains a key research question. In recent years, numerous multimodal causal reasoning datasets have been proposed. Nevertheless, these datasets are either limited in scale or constructed from synthetic images and videos, cartoon-based content, or other non-realistic multimodal sources. To address these limitations, we collect real-world videos and construct DMC-CF-Static, a large-scale benchmark for multimodal causal counterfactual reasoning. Furthermore, to mitigate issues such as data contamination in traditional static evaluation, we represent causal events using causal graphs and propose the Dynamic Graph Intervention (DGI) framework to build the dynamic evaluation benchmark DMC-CF-Dynamic from DMC-CF-Static. Experimental results on the overall DMC-CF, which includes both static and dynamic evaluation benchmarks, demonstrate that the multimodal causal reasoning capabilities of current multimodal large language models in real-world scenarios still require substantial improvement.
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