Question Answering
MEQA: A Benchmark for Multi-hop Event-centric Question Answering with Explanations
However, there's a lack of insight into how these models perform in terms
Overleaf Example
Recent studies have shown promising results on utilizing large pre-trained image-language models for video question answering.
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
Reasoning over TKGs has two challenges: 1.
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
Before that, it is crucial to develop benchmarks to evaluate L VLMs' effectiveness in various medical applications.
CausalChaos for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Understanding the intricate motivations behind human actions is paramount in developing sophisticated systems capable of nuanced behavior analysis.
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram Jungwoo Oh
Despite this remarkable progress, there is a noticeable absence of datasets that combine electrocardiogram (ECG) data with question answering.
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
Visual question answering ( VQA) is a challenging task that requires an in-depth understanding of vision and language, as well as multi-modal reasoning.
Jungo Kasai Keisuke Sakaguchi Y oichi T akahashi Ronan Le Bras Akari Asai
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?