Generative AI with LLMs: Soft Prompt: A Learned and Optimized Input for LLMs

Learn what a soft prompt is and how it can help you fine-tune large language models (LLMs) with minimal resources and maximal performance.

Question

What is a soft prompt in the context of LLMs (Large Language Models)?

A. 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.
B. A strict and explicit input text that serves as a starting point for the model’s generation.
C. A technique to limit the creativity of the model and enforce specific output patterns.
D. A method to control the model’s behavior by adjusting the learning rate during training.

Answer

A. 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.

Explanation

The correct answer is A. 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. A soft prompt is a type of prompt engineering technique that aims to guide a large language model (LLM) to generate desired outputs without modifying the model’s parameters. Unlike a hard prompt, which is a fixed and human-designed text input, a soft prompt is a learned and optimized input that consists of a sequence of embedding vectors. These vectors are initialized randomly and then fine-tuned along with the task-specific objective function. A soft prompt can adapt to different tasks or domains by learning the optimal values for the embedding vectors that can activate the relevant knowledge and skills of the LLM. A soft prompt can also reduce the computational and storage costs of fine-tuning, as well as the risk of overfitting or catastrophic forgetting.

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