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        <title>How Much Knowledge Can You Pack Into the Parameters of a Language Model?</title>
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        <namePart type="given">Adam</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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    <abstract>It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.437</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T How Much Knowledge Can You Pack Into the Parameters of a Language Model?
%A Roberts, Adam
%A Raffel, Colin
%A Shazeer, Noam
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F roberts-etal-2020-much
%X It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models.
%R 10.18653/v1/2020.emnlp-main.437
%U https://aclanthology.org/2020.emnlp-main.437
%U https://doi.org/10.18653/v1/2020.emnlp-main.437
%P 5418-5426

Markdown (Informal)

[How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://aclanthology.org/2020.emnlp-main.437) (Roberts et al., EMNLP 2020)

• How Much Knowledge Can You Pack Into the Parameters of a Language Model? (Roberts et al., EMNLP 2020)

ACL

• Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How Much Knowledge Can You Pack Into the Parameters of a Language Model?. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5418–5426, Online. Association for Computational Linguistics.