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  • Joshua Maynez, Shashi Narayan, Bernd Boh...
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    May 1st, 2020
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    preprint
    Joshua Maynez, Shashi Narayan, Bernd Boh...
    May 1st, 2020

    It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation. In this paper we have analyzed limitations of these models for abstractive document summarization and found that these models are highly prone to hallucinate content that is unfaithful to the input document. We conducted a large scale human evaluation of several neural...

  • 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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