Learn what prompt tuning is and how it can help you adapt large language models to new tasks with minimal resources and maximal performance.
“Prompt Tuning is a technique used to adjust all hyperparameters of a language model.” Is this true or false?
The correct answer is B. False. Prompt tuning is a technique used to adjust a few soft prompts, not all hyperparameters, of a language model. A soft prompt is a set of trainable tokens that are added to a prompt and whose values are updated during additional training to improve performance on specific tasks. Prompt tuning is an efficient, low-cost way of adapting an AI foundation model to new downstream tasks without retraining the model and updating its weights. Prompt tuning can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft prompts, while reducing the computational and storage costs, as well as the risk of overfitting or catastrophic forgetting.
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