Few shot prompting

Technique in natural language processing where a model is given a few examples of the task it needs to perform, helping it generalize from these examples to handle new, unseen data. Few-shot prompting is particularly valuable in scenarios where labeled data is limited, as it allows the model to learn and adapt from minimal input. This method is beneficial for researchers and developers working on machine learning models that require efficient training with limited data resources.

Few shot prompting was discovered on April 16th 2024 and it currently has a search volume of 12.1K with a growth of +99X+.