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How to teach a new task by showing several examples to the model.
Less-exemplary learning (few-shot learning) into prompts to teach a new task to a model, it is a method of inserting several input-sections that show how to do the task. The model removes the mold of the task, the expected format and tone, looking at "in-context" (in-context), without updating weights at all. For example, two or three English sentences before a model and show their official Turkish translation and then when a new sentence is given, the model produces a similar translation by following the bilingual examples. This is a direct result of the overall image recognition and generalization ability gained during the training of the model.
Few-shot learning is dramaticly fast and low cost compared to fine-tuning: without having to train a new model or update weights, it can only be tried and adjusted within minutes by changing prompt. The result of the number of examples and quality directly affects; often choose different and representation samples from each other, help the model to accurately generalize the task. However, examples create a limit in terms of context window and cost, since prompt increases its length, so there may be a more convenient option for a large number of examples or a lot of complex tasks.
Few-shot learning is dramaticly fast and low cost compared to fine-tuning: without having to train a new model or update weights, it can only be tried and adjusted within minutes by changing prompt. The result of the number of examples and quality directly affects; often choose different and representation samples from each other, help the model to accurately generalize the task. However, examples create a limit in terms of context window and cost, since prompt increases its length, so there may be a more convenient option for a large number of examples or a lot of complex tasks.