Scaling up GPT-3 enables strong few-shot NLP task performance without fine-tuning

GPT-3, a 175B parameter autoregressive language model, achieves competitive few-shot learning on diverse NLP tasks by conditioning on task demonstrations in text, without any gradient updates or fine-tuning, showing smooth performance gains with scale.

Based on the paper Language Models are Few-Shot Learners by Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah and 27 others

Challenges of task-specific fine-tuning and the promise of few-shot learning

Modern NLP systems often rely on pre-training large language models followed by fine-tuning on thousands to hundreds of thousands of labeled examples for each specific task, which limits flexibility and requires costly data collection.

Humans can perform new language tasks from just a few examples or instructions, a capability current NLP models largely lack, motivating research into few-shot learning methods that adapt models at inference time without weight updates.

Scaling up language model size may improve in-context learning abilities, allowing models to recognize and perform new tasks from few demonstrations embedded in the input text, potentially reducing the need for task-specific fine-tuning.

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In the rest of this paper

How the method works, step by step, with a flow chart
Three figures from the paper, and how to read them
How it was tested, what was found, and where it falls short

Challenges of task-specific fine-tuning and the promise of few-shot learning

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