meaning of few-shot prompting

A technique used in artificial intelligence, particularly with large language models (LLMs), where a small number of example input-output pairs are provided within the prompt itself. These examples, or 'shots,' guide the model to understand the desired task and generate an appropriate response without requiring extensive fine-tuning or retraining of the model.

By using few-shot prompting, the AI model quickly learned to summarize long articles into concise bullet points.

The developer employed few-shot prompting to adapt the large language model for sentiment analysis on a new dataset.

Etymology: The term 'few-shot prompting' emerged with the advancement and scaling of large language models (LLMs), notably popularized by models like GPT-3. It refers to the ability of these models to perform 'in-context learning,' where they can infer the pattern or task from a few provided examples ('shots') within the input prompt, rather than through traditional training with large datasets (zero-shot learning implies no examples, one-shot implies one example, and few-shot implies a small number).

Cultural Notes: Few-shot prompting represents a significant paradigm shift in how AI models, especially large language models, are interacted with and adapted for new tasks. It has made powerful AI capabilities more accessible to users and developers, allowing for rapid prototyping and deployment of AI solutions without the need for specialized machine learning expertise or vast amounts of labeled data for every new application.

Word Frequency: common

Category: ai

Language: English

Pronunciation: fjuː ʃɒt ˈprɒmptɪŋ