Data annotation is the linchpin of generative AI, as the quality, accuracy, and consistency of annotated data directly influence model performance, making it crucial yet complex.
Generative AI models like GPT require extensive labeled data across various types of unstructured and semi-structured elements, necessitating distinct annotation strategies for text, images, and audio.
Text annotation involves tagging entities and sentiments, while image annotation requires techniques such as polygonal segmentation, illustrating the diverse strategies needed for different data types.
Tools like Labelbox and CVAT are essential for effective data annotation, as they help structure the vast amounts of varied data necessary for training generative AI models.
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