Language models are few-shot learners
OpenAI publishes foundational research on few-shot learning capabilities in language models. LLMs can perform tasks with minimal examples without fine-tuning, revealing emergent rapid adaptation capacity.
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OpenAI publishes foundational research on few-shot learning capabilities in language models. LLMs can perform tasks with minimal examples without fine-tuning, revealing emergent rapid adaptation capacity.
OpenAI releases analysis showing that since 2012, compute required to train a neural network to equivalent ImageNet classification performance halves every 16 months. Training to AlexNet-level performance now requires 44x less compute than 2012 — far exceeding Moore's Law's predicted 11x improvement. Algorithmic progress outpaces classical hardware efficiency for heavily-invested AI tasks.