Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Much Knowledge Can You Pack Into the Parameters of a Language Model?   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 at https://goo.gle/t5-cbqa.

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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg