Prompt tuning involves adjusting pre-trained models using task-specific prompts, be it human-engineered (hard prompts) or AI-generated (soft prompts).
Soft prompts are fake tokens added to input sequences, guiding the model's processing. This method is more efficient than traditional fine-tuning for model adaptation to new tasks.
Prompt tuning is versatile, applicable to natural language processing, image categorization, and code creation. It offers interpretability by allowing examination of prompt parameters.
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