Generative AI with LLMs: Prompt Tuning: A Parameter-Efficient Technique for Language Models

Learn what prompt tuning is and how it can help you adapt large language models to new tasks with minimal resources and maximal performance.

Question

“Prompt Tuning is a technique used to adjust all hyperparameters of a language model.” Is this true or false?

A. True
B. False

Answer

B. False

Explanation

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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