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.
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.
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
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.
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.
- 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.
- 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.
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.
About the authorSoftware Mind
Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world.