Prompt tuning is a parameter-efficient adaptation method that learns a small set of task-specific vectors, often called a soft prompt or prefix, while leaving most or all base model parameters frozen. The learned prompt is prepended to model inputs to improve performance on a defined task at lower cost than full fine-tuning.
A parameter efficient tuning mechanism that learns a "prefix" that the system prepends to the actual prompt. One variation of prompt tuning---sometimes called prefix tuning ---is to prepend the prefix at every layer . In contrast, most prompt tuning only adds a prefix to the input layer. For prompt tuning, the "prefix" (also known as a "soft prompt") is a handful of learned, task-specific vectors prepended to the text token embeddings from the actual prompt. The system learns the soft prompt by freezing all other model parameters and fine-tuning on a specific task.
Prompt tuning is a parameter-efficient fine-tuning method used to improve a generative AI model's performance on a specific task. It involves learning a "prefix" that is prepended to the actual prompt, sometimes at every layer. This approach is considered cheaper and faster than other tuning methods, often yielding good results. Prompt tuning is particularly effective when you have a specific task and want the model to perform it in a certain way. It's also sometimes referred to as prompt learning or parameter-efficient (fine) tuning. For more information, see Introduction to prompt engineering.
A side-by-side comparison of Prompt Engineering and Prompt Tuning. Understand how human-designed model inputs differ from learned task-specific soft prompts.
A side-by-side comparison of Prompt Tuning and Fine-tuning. Understand how a learned soft prompt differs from retraining a pretrained model on a specialized dataset.