Soft Prompt Tuning

Soft Prompt Tuning is a parameter-efficient fine-tuning technique that optimizes continuous, learnable prompt embeddings rather than discrete text tokens to adapt pre-trained language models for specific tasks. This method introduces trainable prompt parameters as virtual tokens that are prepended to input sequences, allowing models to learn task-specific representations without modifying the original model weights. Soft prompt tuning requires significantly fewer computational resources than full fine-tuning while achieving comparable performance across various downstream tasks. The technique maintains the original model’s general capabilities while enabling specialized behavior through learned prompt representations. Advanced implementations utilize gradient-based optimization to discover optimal prompt embeddings, support multi-task learning scenarios, and enable rapid adaptation to new domains with minimal training data requirements, making it particularly valuable for resource-constrained environments and production deployments.