question-answer pairs
Question-answer pairs are structured formats used in supervised learning, especially in Natural Language Processing (NLP), where the model learns to generate answers based on the provided questions. This setup is integral to applications like chatbots and automated QA systems.
- CineTechBench: A Benchmark for Cinematographic Technique Understanding and Generation
- EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMs
- GRIP: A Graph-Based Reasoning Instruction Producer
- MONITRS: Multimodal Observations of Natural Incidents Through Remote Sensing
- MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal LLMs
- NavBench: Probing Multimodal Large Language Models for Embodied Navigation
- SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
- SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
- ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
- Situat3DChange: Situated 3D Change Understanding Dataset for Multimodal Large Language Model
- VideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding