Prompt learning is Here to Repurpose Your AI Model Without New Parameters

Prompt learning allows engineers to achieve the same ends to the AI model without requiring new parameters

Foundation models in AI are algorithms that train and develop with broader datasets to execute various functions. Moreover, Artificial Intelligence is going through a tremendous number of evolutions. Further, foundation models are built on conventional deep learning and transfer learning algorithms. Therefore, foundation models in AI give rise to new capabilities for efficiently implementing tasks. Foundation models are initially trained with massive amounts of unstructured data and then fine-tuned with labeled data for specific tasks. However, this approach requires introducing new parameters into the model. For example, fine-tuning a large language BERT model to perform binary classification would require an additional set of 1,024 x 2 labeled parameters.

In contrast, prompt learning allows engineers to achieve the same ends without requiring new parameters. Instead, natural language text cues, called "prompts" are injected into the AI model's inputs during the pre-training phase. Their purpose is to provide context for a variety of potential downstream tasks proactively.

What is Prompt-Based Learning?

Prompt-based learning is an emerging group of ML model training methods. In prompting, users directly specify the task they want to be completed in natural language for the pre-trained language model to interpret and complete. This contrasts with traditional Transformer training methods where models are first pre-trained using unlabelled data and then fine-tuned, using labeled data, for the desired downstream task.

A prompt is essentially an instruction written in natural language by the user for the model to execute or complete. Depending on the complexity of the task being trained for, several prompts may be required. Creating the best prompt, or series of prompts, for the desired use task is a process known as 'prompt engineering'.

Supervised learning, where AI models are trained on input data annotated for a particular output until they can detect the underlying relationships between the inputs and outputs, plays a major role in natural language processing (NLP). Early NLP models relied heavily on feature engineering — researchers used domain knowledge to extract key information from training datasets and provide models with the guidance needed to learn from the data. But with the advent of neural network models for NLP, the focus pivoted from feature engineering to model architecture engineering. Neural networks enabled features to be learned jointly with the training of the models themselves.

Now the paradigm in NLP is shifting again in favor of an approach some researchers call "prompt-based learning." Given a range of carefully designed prompts, a language model trained in an unsupervised fashion- that is, on unlabeled data- can be used to solve several tasks. But there's a catch here — prompt-based learning requires finding the most appropriate prompt to allow a language model to solve the task at hand.

How Does Prompt Learning repurpose an AI model?

Prompt-based learning has numerous advantages over the traditional pre-train, fine-tune paradigm. The biggest advantage is that prompting generally works well with small amounts of labeled data. With GPT-3, for example, it's possible to achieve strong performance on certain tasks with only one labeled example.

Indeed, research has shown that a single prompt may be comparable to training with 100 conventional data points. This suggests that prompting could enable a massive advance in training efficiency, meaning less cost, less energy expended, and faster time to value with AI models. This makes prompt-based learning a tantalizing prospect for many businesses seeking to leverage and train their NLP models. However, there are some challenges.

Challenges with Prompts

Prompts can be designed either manually or through automated methods. But creating the perfect prompt requires both understanding a model's inner workings and trial and error.