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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.


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

A. True
B. False


B. 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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Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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