meaning of few-shot prompting

A technique used in natural language processing, particularly with large language models (LLMs), where a small number of example input-output pairs (the 'shots') are included directly within the prompt itself. This method allows the model to learn the desired task or behavior from these examples without requiring extensive fine-tuning or retraining on a large dataset.

By using few-shot prompting, we could rapidly adapt the large language model to a new sentiment analysis task with just three examples.

Few-shot prompting has become a crucial technique for making large language models more versatile and adaptable to various downstream tasks without retraining.

Synonyms: in-context learning (in this context), example-based prompting

Etymology: Coined in the context of large language models, combining 'few-shot learning' (a concept from general machine learning referring to learning from limited examples) with 'prompting' (the act of instructing an LLM through natural language input). It gained prominence with models like GPT-3 that exhibited strong in-context learning capabilities.

Cultural Notes: Few-shot prompting is a foundational concept in the practical application and research of modern large language models. It represents a significant shift from traditional machine learning paradigms, where extensive labeled datasets were typically required for task-specific training. This technique highlights the emergent ability of very large models to learn new tasks from a handful of examples embedded directly in the input, making them highly adaptable and reducing the need for costly and time-consuming fine-tuning processes. It is a key enabler for rapid prototyping and deployment of AI solutions across diverse domains.

Word Frequency: common

Category: ai

Language: English

Pronunciation: /ˈfjuːʃɒt ˈprɒmptɪŋ/