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Language Models are Few-Shot Learners
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title
Language Models are Few-Shot Learners
(English)
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main subject
prompt engineering
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language model
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GPT-3
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author
Ilya Sutskever
series ordinal
30
object named as
Ilya Sutskever
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Arvind Neelakantan
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7
object named as
Arvind Neelakantan
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Alec Radford
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29
object named as
Alec Radford
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Sam McCandlish
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28
object named as
Sam McCandlish
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Rewon Child
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15
object named as
Rewon Child
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Dario Amodei
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31
object named as
Dario Amodei
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Amanda Askell
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10
object named as
Amanda Askell
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Girish Sastry
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9
object named as
Girish Sastry
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Sandhini Agarwal
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11
object named as
Sandhini Agarwal
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Prafulla Dhariwal
series ordinal
6
object named as
Prafulla Dhariwal
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Christopher Berner
series ordinal
27
object named as
Christopher Berner
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Pranav Shyam
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8
object named as
Pranav Shyam
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Tom B. Brown
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1
object named as
Tom B. Brown
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Benjamin Mann
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2
object named as
Benjamin Mann
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Melanie Subbiah
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4
object named as
Melanie Subbiah
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Nick Ryder
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3
object named as
Nick Ryder
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Daniel Ziegler
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17
object named as
Daniel M. Ziegler
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Jeffrey Wu
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18
object named as
Jeffrey Wu
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Tom B. Brown
series ordinal
1
object named as
Tom Brown
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Daniel Ziegler
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17
object named as
Daniel Ziegler
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Jared Kaplan
series ordinal
5
object named as
Jared Kaplan
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Arvind Neelakantan
series ordinal
7
object named as
Arvind Neelakantan
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Ari Herbert-Voss
series ordinal
12
object named as
Ariel Herbert-Voss
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Gretchen Krueger
series ordinal
13
object named as
Gretchen Krueger
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author name string
Tom Henighan
series ordinal
14
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Aditya Ramesh
series ordinal
16
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Clemens Winter
series ordinal
19
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Christopher Hesse
series ordinal
20
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Mark Chen
series ordinal
21
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Eric Sigler
series ordinal
22
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Mateusz Litwin
series ordinal
23
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Scott Gray
series ordinal
24
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Benjamin Chess
series ordinal
25
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Jack Clark
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26
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Jared D Kaplan
series ordinal
5
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Chris Hesse
series ordinal
20
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language of work or name
English
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publication date
28 May 2020
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number of pages
75
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official website
https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
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full work available at URL
https://arxiv.org/pdf/2005.14165.pdf
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https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
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published in
arXiv
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Advances in Neural Information Processing Systems 33
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describes a project that uses
GPT-3
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TriviaQA
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Natural Questions
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WebQuestions
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SuperGLUE
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cites work
Learning to learn by gradient descent by gradient descent
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Semi-supervised Sequence Learning
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Natural Questions: A Benchmark for Question Answering Research
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Identifiers
DOI
10.48550/ARXIV.2005.14165
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arXiv ID
2005.14165
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Google Scholar paper ID
15953747982133883426
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ResearchGate publication ID
341724146
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Semantic Scholar corpus ID
218971783
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Semantic Scholar paper ID
6b85b63579a916f705a8e10a49bd8d849d91b1fc
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