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2 resources

  • Jason Wei, Xuezhi Wang, Dale Schuurmans,...
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    Jan 10th, 2023
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    preprint
    Jason Wei, Xuezhi Wang, Dale Schuurmans,...
    Jan 10th, 2023

    We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of...

  • Rohan Anil, Andrew M. Dai, Orhan Firat
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    May 17th, 2023
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    preprint
    Rohan Anil, Andrew M. Dai, Orhan Firat
    May 17th, 2023

    We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more...

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