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  1. Prompting is a technique used to prime a pretrained language model for a specific downstream task by including a text prompt that describes the task or demonstrates an example of the task. This method allows you to use the same frozen pretrained model for multiple tasks, making it more efficient than training separate models for each task.

    Types of Prompting Methods

    There are two main categories of prompting methods:

    1. Hard Prompts: These are manually crafted text prompts with discrete input tokens. Creating effective hard prompts requires significant effort and expertise.

    2. Soft Prompts: These are learnable tensors concatenated with the input embeddings. Soft prompts can be optimized to a dataset but are not human-readable.

    Soft Prompt Methods

    Several soft prompt methods are supported by the Hugging Face PEFT library:

    • Prompt Tuning: This method involves training a smaller set of task-specific prompt parameters while keeping the pretrained model's parameters frozen. It is particularly useful for text classification tasks.

    • Prefix Tuning: Designed for natural language generation tasks, prefix tuning prepends a sequence of task-specific vectors to the input. These vectors are optimized by a separate feed-forward network.

    • P-Tuning: Suitable for natural language understanding tasks, P-tuning adds trainable embedding tensors that can be inserted anywhere in the input sequence. It uses a prompt encoder to optimize the prompt parameters.

    • Multitask Prompt Tuning: This method learns a single prompt from data for multiple task types, enabling parameter-efficient transfer learning.

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