Prompt Learning: Soft vs. Hard Prompt Approaches

Prompt Learning: Soft Prompt vs. Hard Prompt
Prompt learning, also known as prompt tuning, is a recently emerged technique in natural language processing (NLP) that allows models to learn from limited supervision. This method utilizes pre-trained language models to generate responses to a given prompt, and then fine-tunes the model using the generated responses. Prompt learning has shown impressive results in various NLP tasks, including text classification, question answering, and dialog generation. In this article, we will focus on the two main types of prompts used in prompt learning: soft prompts and hard prompts.
Soft Prompts
Soft prompts are generated by modifying the input to the pre-trained language model in a controlled way. The goal is to guide the model to produce responses that are consistent with the desired output while preserving the context of the original input. Soft prompts can be categorized into two main categories: template-based prompts and context-based prompts.
Template-based prompts involve using predefined response templates or patterns that are adjusted based on the context of the input. For example, in question answering, a common template is “The answer to [question] is [answer].” By replacing [question] with the actual question and [answer] with the desired answer, the model can be prompted to generate responses consistent with the desired output.
Context-based prompts, on the other hand, utilize the context of the input to generate the prompt. These prompts are typically generated by leveraging the attention mechanism of the pre-trained language model to focus on important aspects of the input. By attending to different parts of the input, the model can be prompted to generate responses that focus on specific aspects of the input.
Hard Prompts
Hard prompts, also known as raw text prompts, involve directly providing the desired output as the prompt to the pre-trained language model. These prompts are typically used when the desired output is available or can be easily generated. Hard prompts have shown impressive results in tasks such as dialog generation and machine translation, where there is a clear mapping between the input and desired output.
One common approach to generate hard prompts is to use a pre-defined set of rules or heuristics to construct the prompt. For example, in machine translation, a hard prompt could be generated by translating the source text to the target language using a translation tool. By providing the translated text as the prompt, the model can be prompted to generate responses that match the translation.
Comparison between Soft Prompts and Hard Prompts
Soft prompts and hard prompts have their respective advantages and disadvantages. Soft prompts provide more flexibility in terms of guiding the model to generate responses that are consistent with the desired output while preserving the context of the original input. However, soft prompts require more effort in terms of designing appropriate templates or strategies for generating the prompts.
On the other hand, hard prompts provide a direct mapping between the input and desired output, making it easier to generate accurate responses. However, hard prompts may not always be available or easy to generate for every task. Additionally, hard prompts may not充分利用到soft prompt的优点，比如可以让模型自己探索新的表达方式，而不仅仅是复制粘贴已知的答案。