Zero and Few-shot Prompting

What is Zero and Few-Shot Prompting?

Zero-shot and few-shot prompting are techniques used in natural language processing (NLP) models to generate desired outputs without explicit training on specific tasks.

Zero-Shot Prompting

In zero-shot prompting, a prompt that is not part of the training data is provided to the model, but the model can still generate a desired result. This technique makes large language models useful for many tasks without requiring task-specific training.

Few-Shot Prompting

While large language models demonstrate remarkable zero-shot capabilities, they may fall short on more complex tasks when using the zero-shot setting. Few-shot prompting is a technique that enables in-context learning by providing demonstrations in the prompt to steer the model towards better performance. These demonstrations serve as conditioning for subsequent examples where the model is expected to generate a response. Few-shot prompting first appeared when models were scaled to a sufficient size.

Example

Consider a task where the model is asked to correctly use a new word in a sentence. In zero-shot prompting, the model is given a definition and asked to create a sentence without any examples. In few-shot prompting, the model is provided with one or more examples of sentences using the new word correctly, which helps guide the model's response.

Limitations and Advanced Prompting Techniques

Standard few-shot prompting works well for many tasks but may not be perfect for more complex reasoning tasks. In such cases, more advanced prompting techniques, such as zero-shot chain of thought or few-shot chain of thought, can be employed. These techniques involve guiding the model through a series of reasoning steps to arrive at the correct answer.

Zero-shot and few-shot prompting are techniques that allow NLP models to generate desired outputs without explicit training on specific tasks. Zero-shot prompting provides a prompt without examples, while few-shot prompting includes demonstrations to guide the model's response. For more complex tasks, advanced prompting techniques may be necessary to achieve better results.