Computer Vision
Datasets for training computer vision AI models encompass diverse images annotated with labels like object classes, bounding boxes, and semantic segmentation masks. These datasets serve as the foundation for teaching machines to perceive and understand visual information, enabling applications ranging from autonomous driving to medical imaging diagnostics.
Natural Language
Datasets for training Language Model AI, particularly Large Language Models (LLMs), demand vast corpora of text with diverse linguistic structures and contexts. Annotated with tasks like language modeling, text completion, and sentiment analysis, these datasets empower LLMs to generate coherent, contextually relevant text across various domains and applications.
Instruction Following
Datasets for training Instruction Following AI models entail collections of diverse instructions paired with corresponding actions or outcomes. These datasets enable AI systems to understand and execute human-provided instructions accurately across a range of tasks, from robotic manipulation to virtual assistants, facilitating seamless human-computer interaction and task automation.
Human Preferences
Datasets for training Large Language Models (LLMs) with human preferences prioritize gathering human feedback on generated outputs, ensuring performance and safety. RLHF (Reward Learning from Human Feedback) datasets integrate human evaluations into reinforcement learning frameworks, guiding LLMs to produce more aligned and beneficial outputs, crucial for applications like conversational agents and content generation.
