Zero-Shot and Few-Shot Learning with LLMs

Author(s): Michał Oleszak

Originally published on Towards AI.

Guidelines for optimal use.

Chatbots based on Large Language Models (LLMs), such as OpenAI’s ChatGPT, show an astonishing capability to perform tasks for which they have not been explicitly trained. In some cases, they can do it out of the box. In others, the user must specify a few labeled examples for the model to pick up the pattern.

Two popular techniques for helping a Large Language Model solve a new task are zero-shot and few-shot prompting. In this article, we’ll explore how they work, see some examples, and discuss when to use (and, more importantly, when not to use) zero-shot and few-shot prompting.

This article was first published on the neptune.ai blog.

The goal of zero-shot and few-shot learning is to get a machine-learning model to perform a new task it was not trained for. It is only natural to start by asking: what are the LLMs trained to do?

LLMs used in chatbot applications typically undergo two training stages. In pre-training, they learn to predict the next word. During fine-tuning, they learn to give specific responses. | Source: Author

Most LLMs used in chatbots today undergo two stages of training:

In the pre-training stage, the model is fed a large corpus of text and learns to predict the… Read the full blog for free on Medium.

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Published via Towards AI