Perplexity
Perplexity is a quantitative evaluation metric used in natural language processing that measures the degree of uncertainty or surprise of a language model in predicting a test sequence, defined as the exponential of the average negative log-likelihood per token.
339
Publications
27K
Citations
875
Authors
271
Institutions
Publications per year
2017–2026
108
Parents
339
Neural Architecture Search with Reinforcement Learning
Barret Zoph, Quoc V. Le · arXiv (Cornell University) · 2016 · 3.9K citations · Full text
The design for the wall street journal-based CSR corpus
Douglas B. Paul, Janet M. Baker · 1992 · 1.1K citations · Full text
Exploring the Limits of Language Modeling
Rafał Józefowicz, Oriol Vinyals, Mike Schuster et al. · arXiv (Cornell University) · 2016 · 919 citations · Full text
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Carnegie Mellon University
Pittsburgh, United States
15 h-index
60 publications
3.8K citations
Microsoft (United States)
Redmond, United States
10 h-index
20 publications
1.8K citations
Google (United States)
Mountain View, United States
9 h-index
25 publications
7.2K citations

Johns Hopkins University
Baltimore, United States
8 h-index
17 publications
968 citations
Massachusetts Institute of Technology
Cambridge, United States
8 h-index
21 publications
1.5K citations
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
6 h-index
23 publications
2.1K citations
University of Cambridge
Cambridge, United Kingdom
6 h-index
13 publications
2.5K citations
University of Washington
Seattle, United States
6 h-index
9 publications
2.3K citations
1–8 of 271
Repository · 47 publications · 9.6K citations
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Conference · 6 publications · 252 citations
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Repository · 4 publications · 365 citations
4
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3 publications · 172 citations
3
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TASLP · 3 publications · 81 citations
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