Transformers Enable Few-Shot Learning in NLP

Large-scale Transformer models can perform new tasks with minimal examples through prompt-based learning.

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🤯 Did You Know (click to read)

GPT-3 can perform diverse tasks using only a few input-output examples included in the prompt.

Models like GPT-3 use self-attention and massive pretraining to generalize to new tasks with few-shot, one-shot, or zero-shot prompts. The model can infer patterns from a few examples in the prompt without task-specific fine-tuning, enabling applications in translation, summarization, and question answering.

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💥 Impact (click to read)

Few-shot learning reduces the need for extensive labeled datasets, accelerating AI deployment across multiple NLP tasks.

Developers and researchers can quickly test new applications and deploy AI solutions efficiently, even with limited data.

Source

Brown et al., 2020 - GPT-3

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