Re:infer's machine learning models use an architecture called the Transformer, which over the past few years has achieved state of the art results on the majority of common natural language processing (NLP) tasks. The go-to approach has been to take a pre-trained Transformer language model and fine-tune it on the task of interest.
More recently, we have been looking into 'prompting'—a promising group of methods which are rising in popularity. These involve directly specifying the task in natural language for the pre-trained language model to interpret and complete.
Prompt-based methods have significant potential benefits, so should you use them? This post will:
- Illustrate the difference between traditional fine-tuning and prompting.
- Explain the details of how some popular prompt-based methods work.
- Discuss the pros and cons of prompt-based methods, and provide our recommendation on whether or not to use them.