Prompt-based learning is an emerging class of ML model training techniques. It allows users to specify the task they want the pre-trained language model to interpret and complete in natural language. It is in contrast to traditional transformer training methods, in which models are first pre-trained using unlabeled data and then fine-tuned for the desired downstream task using labeled data.

 

A prompt is a natural language instruction written by the user for the model to execute or complete.

 

Several prompts may be required, depending on the complexity of the trained task. Prompt engineering is the process of developing the best prompt, or series of prompts, for the desired use task. In natural language processing, supervised comprehension, training AI models on input data annotated for a specific output until they can detect the underlying relationships between the inputs and outputs plays a significant role (NLP). Researchers used domain knowledge to extract information from training datasets and provide models with the guidance needed to learn from the data in early NLP models.

 

However, with the introduction of neural network models for NLP, the emphasis shifted from feature engineering to model architecture engineering. Neural networks enabled features to be learned concurrently with model training.

 

A language model trained unsupervised—that is, on unlabeled data—can solve various tasks when given a set of carefully designed prompts. However, there is a catch: learning prompt-based necessitates the selection of the most appropriate prompt to allow a language model to solve the task at hand.

 

Prompt learning repurposes an AI model in what way?

The traditional pre-train, fine-tune paradigm has numerous advantages over prompt-based learning. The most significant advantage is that prompting works well with small amounts of labeled data. With GPT-3, for example, it is possible to achieve high performance on specific tasks with just one labeled example. Indeed, studies have shown that a single prompt can be as effective as training with 100 conventional data points. It suggests that prompting could significantly improve training efficiency, resulting in lower costs, less energy expended, and a faster time to value for AI models. For many businesses looking to leverage and train their NLP models, prompt-based learning is a prospect. However, there are some difficulties.

 

Prompt Learning Challenges

Prompts can be created either manually or automatically. But, creating the perfect prompt requires understanding a model's inner workings and trial and error.