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.

Why was the dataset created? Has the dataset been used already? QA dataset has already been used. QA establishes a framework to benchmark question answering at the present time: answers (e.g., the number of Shohei Ohtani's home runs) change in real time. This could also include the system's interactions with its information retrieval module (for How many instances are there?