How and Why Soft Prompts Are Slowly Replacing Text Prompts

It is rare for a word that originated in the first half of the 14th century to gain a new meaning these days and become a highly popular phrase in almost every language. However, this is exactly what has happened to the word “prompt”, which is now primarily used to refer to instructions and commands for generative models (better known as AI models or Large Language Models, click to read more about LLM use cases).

Currently, most people — chances are you are part of that group as well — think of prompts as text-based entries created in a human language. But there are other types of prompts already being used that are much more sophisticated and, as you would expect, very powerful.

What’s the catch? They are not human-readable. Read on to learn more.

What are soft prompts?

Generally speaking, soft prompts can be described as a concept that involves incorporating vectors into an input sequence and then fine-tuning these vectors while keeping the rest of the pre-trained model’s components unchanged.

So, to create a soft prompt, you need to adjust these vectors while keeping most of the pre-trained weight static. The modified input sequence, defined by the fine-tuned vectors, will then guide the model’s behavior for a particular task.

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What makes soft prompts particularly intriguing is their design, which is very difficult for humans to interpret.

Unlike traditional human-readable prompts, which provide clear instructions expressed in human languages, soft prompts involve incorporating vectors that are very much abstract and random. In other words, these vectors lack a direct linguistic or semantic connection to the task at hand. Therefore, even though these vectors guide the model’s behavior, their non-linguistic nature makes it very hard for humans to understand them.

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How do soft prompts work?

To grasp the fundamental idea behind soft prompting, let’s examine how a model comprehends a prompt like “A famous actor playing a guitar”.

Firstly, the prompt is dissected into individual tokens: “A”, “famous,” “actor,” “playing,” “a” and “guitar”.

Subsequently, all tokens, one by one, are converted into vectors of values, which can be thought of as model parameters.

Then, the model itself can be further adjusted (or trained, to be more specific) by adjusting the values.

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Once we begin changing the weights, the token vectors will no longer match the real vocabulary-based meanings. This is exactly why soft prompts are so difficult to interpret by humans.

The 4 differences between soft prompts and ‘regular’ prompts

1. Approach

When working with regular prompts, the process involves providing the model with a specific input, and in response, the model generates an output based on its pre-existing knowledge and contextual understanding.

The concept of soft prompts takes a slightly different path. In this case, the focus is on modifying the prompt itself without making changes to the core knowledge of the main model. Essentially, soft prompts endeavor to fine-tune only the prompt, rather than the entire model.

2. Flexibility

Crafting regular prompts requires careful consideration for each specific task to achieve optimal outcomes.

On the other hand, soft prompts are flexible and can be easily changed for different tasks. You can do this without completely changing the whole model, which provides a lot of flexibility in how you handle tasks.

3. Task Adaptation

Regular prompts are usually customized for specific tasks, which could mean making unique prompts or even building different models for each task to get the best outcomes.

That said, soft prompts are highly adaptable. By tweaking prompts, you can use the same main model for various tasks, making it easy to switch between tasks without any disruptions.

4. Token Length

Regular prompts can be really long, especially if the task is complex. Soft prompts are more concise in the number of words they use. This is especially true when they’re used for many tasks with the same model. This precision can make it much easier to handle tasks and improve how they perform.

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Hard prompts and soft prompts — what’s the difference?

Hard Prompts involve providing specific and explicit instructions or queries to the model. They are crafted in a human-readable format and are designed to directly guide the model’s response.

When using hard prompts, a model’s behavior is directly controlled by the input instructions — although some fine-tuning may be required to achieve optimal performance on different tasks.

So, the difference between hard prompts and soft prompts is that hard prompts provide explicit instructions to guide the model’s responses and might require tailored models for different tasks, while soft prompts offer a more versatile and efficient strategy by adjusting prompt parameters to guide a model’s behavior across various tasks.

What are the applications of soft model prompting?

One of the biggest benefits of soft prompts is that they make multi-task learning easier. Unlike traditional methods that require separate adjustments for different tasks, soft prompts let a single model switch between tasks smoothly just by changing the prompts. By utilizing this method, it is possible to save both time and resources, while also preserving the model’s knowledge.

Soft prompting has a wide range of applications, including analyzing sentiments, answering questions, translating languages and summarizing texts.

In the case of chatbots and conversational agents, for example, soft prompts offer a particular advantage. By tweaking prompts, these agents can customize their responses for different personalities or styles and create more engaging interactions.

What is the difference between prefix tuning and soft prompt tuning?

As you can guess, soft prompt tuning and prefix tuning are two different techniques used in machine learning to fine-tune models for specific tasks. Both have their strengths and weaknesses, and the choice of which to use will depend on the specific case and desired outcomes.

Prefix tuning involves adding a specific prefix to input text to guide the model towards generating more accurate outputs. This technique is useful when the desired outcome is to generate text that is highly relevant to a specific topic or context.

And so, without having to explain what soft prompt tuning is at this point, we can say that the main difference between prefix tuning and soft prompt tuning is that prefix tuning is used to generate something that is more accurate with the concept behind the prompt, while soft prompt tuning is about creating more diverse outputs based on a more general prompt.

What is the difference between soft prompts and LoRA?

Soft prompts and LoRA are different technologies that are used in two different ways.

The LoRA technique involves understanding a matrix that represents the rank composition, which is created by reducing the weight matrix in a transformer.

Conversely, prompt tuning relies on using a soft prompt that is encoded within the model to be learned, rather than relying on a predefined hard prompt given by a person for the task.

Even though both methods can be helpful in model learning, prompt tuning is generally considered more effective.

Final thoughts regarding soft promts

Some say that soft prompts are driving a big revolution in the AI landscape by offering a highly dynamic approach to task guidance

Unlike the ‘traditional’ prompts that are based on explicit instructions, soft prompts involve adjusting prompt parameters while preserving the core message. This flexibility empowers a single model to efficiently perform multiple tasks, which reduces the need for extensive fine-tuning or the creation of other models.

Taking into account that AI has already accelerated the work of many business environments around the world, we can expect that soft prompt tuning — contrary to what its name suggests — will have a strong impact on the development of AI-based tools and on how prompt creators will use those tools. It’s only a matter of time before you’ll hear this term more often in conversations about large language models. To learn more about AI development opportunities, fill out the contact form.

FAQ section

What is the difference between fine-tuning and prompting LLM?

Fine-tuning and prompting are two distinct methods used to modify and control large language models (LLMs). Fine-tuning involves training a pre-trained model on specific tasks or datasets, which make it more specialized for particular applications. It refines the model’s parameters to improve performance on specific tasks. The term prompting describes providing input text, also known as prompts, to direct the model’s output (answers). Users can influence a LLM’s responses by carefully crafting prompts without altering the existing model. Fine-tuning a model requires having access to its architecture, as well as a significant number of computational resources. At the same time, prompting is a more accessible and user-friendly approach for most applications, as it enables users to interact with a model without the need for extensive training or reprogramming.

Read also: What are LLM hallucinations?

How do soft prompts ensure precision and accuracy in AI-generated outputs?

Soft prompts are crucial in ensuring precision and accuracy in AI-generated outputs by guiding a model’s response in a specific direction. Unlike so- called hard prompts, which strictly define the input context, soft prompts provide general guidance while giving a model some flexibility in interpretation. This available space enables a model to generate more precise and accurate responses because it can adapt its output based on the context provided by the soft prompt. Soft prompts guide the model’s attention to focus on input aspects aligned with user intentions. By balancing guidance and flexibility, soft prompts empower users to influence the output without setting strict rules, which result in more contextually accurate generative AI responses.

Can soft prompts be combined with other AI optimization techniques?

Soft prompts can be combined with various AI optimization techniques to enhance the overall performance and precision of AI-generated outputs. Soft prompts can guide model responses based on feedback, thereby enabling iterative learning and continuous improvements in accuracy and relevance when integrated with techniques such as reinforcement learning. Combining soft prompts with solutions such as active learning allows a model to select informative examples for training, which enhances its understanding of specific contexts. Employing soft prompts alongside adversarial training approaches strengthens a model’s resilience against diverse inputs, ensuring robustness and accuracy in different scenarios. Developers can create more adaptable AI systems by synergizing soft prompts with optimization techniques tailored to user needs and specific applications.

What industries or sectors can benefit the most from soft prompting techniques?

Prompting techniques can benefit several industries or sectors, particularly those using natural language processing (NLP) and human-AI interaction. Let’s name a few, starting with customer support and services, where soft prompts enable an in-depth understanding of user queries, which enhance chatbots and customer interactions. The content creation industry can improve its output by generating customized articles, promotional materials, and creative content, while ensuring texts are free of any spelling, grammatical, or punctuation errors. Additionally, online education platforms can provide tailored learning experiences that cater to students’ individual needs. The legal and research sectors can use data analysis and contextual information extraction to their advantage. In addition, including soft prompts in gaming and user interfaces can enhance human-computer interaction, resulting in more engaging and responsive experiences.