We utilize an open and widely used data format, i.e., JSON format, for the MEQA dataset. "context": "Roadside IED kills Russian major general [...]", # The context of the question "question": "Who died before AI-monitor reported it online?", "What event contains Al-Monitor is the communicator? "What event is after #1 has a victim? "Who died in the #2? major general,local commander,lieutenant general" We present a list of Datasheets [Gebru et al., 2021] for the MEQA dataset, synthesizing many of the For what purpose was the dataset created?

However, there's a lack of insight into how these models perform in terms

Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.

Reasoning over TKGs has two challenges: 1.

Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.

Understanding the intricate motivations behind human actions is paramount in developing sophisticated systems capable of nuanced behavior analysis.

Despite this remarkable progress, there is a noticeable absence of datasets that combine electrocardiogram (ECG) data with question answering.

Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.