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Introduces GPT-3, a 175-billion parameter language model demonstrating groundbreaking few-shot learning capabilities without fine-tuning.

Overview

This seminal paper by OpenAI introduces GPT-3, a massive language model with 175 billion parameters, showcasing unprecedented few-shot learning. It demonstrates that by scaling up transformer models, they can perform a wide range of NLP tasks—from translation to question answering—effectively by simply being given a few examples as a prompt, without any gradient updates or fine-tuning. The research highlights the significant progress in NLP achieved through model scale and explores both the impressive capabilities and the critical ethical implications of such powerful AI systems, setting a new benchmark for general-purpose language understanding and generation.

Abstract

We show that scaling up language models vastly improves their performance on a wide variety of NLP tasks. Our largest model, GPT-3, with 175 billion parameters, is 10x larger than any previous non-sparse language model. We find that GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, but still struggles on some tasks like commonsense reasoning. Crucially, GPT-3 achieves this performance in a "few-shot" setting—without any gradient updates or fine-tuning. It can adapt to new tasks simply by being given a few examples as a prompt, performing far better than previous models on difficult tasks like question answering and machine translation. We also discuss the broader societal impacts of our work, including potential risks and ethical considerations.