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Large language models (LLMs) have a substantial capacity for high-level analogical reasoning: reproducing patterns in linear text that occur in their training data (zero-shot evaluation) or in the provided context (few-shot in-context learning).
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Calibrate before use: Improving few-shot performance of language models
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Multi-stage prompting for knowledgeable dialogue generation
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PaLM: Scaling language modeling with pathways
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Scaling instruction-finetuned language models
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