Auto labeling is an AI-driven process that predicts labels, reducing or eliminating the need for human involvement in the labeling process. Examples of this include model-assisted labeling and active learning-based labeling. LabelGPT has advanced this process by using a combination of multiple foundation models to achieve zero-shot labeling.
LabelGPT works by leveraging multiple foundational models to predict labels without any prior examples or training on the specific task. This process, known as zero-shot labeling, has the potential to significantly streamline and automate the labeling process.
Key takeaways:
Auto labeling is an AI process that predicts labels, reducing or replacing human involvement in the labeling process.
Model-assisted labeling and active learning based labeling are examples of auto labeling.
LabelGPT has advanced the process by using a combination of multiple foundation models to achieve zero shot labeling.
LabelGPT is currently in a waiting list phase with over 5000 users.
