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Measuring Massive Multitask Language Understanding
v1v2v3 (latest)

Measuring Massive Multitask Language Understanding

International Conference on Learning Representations (ICLR), 2020
7 September 2020
Dan Hendrycks
Collin Burns
Steven Basart
Andy Zou
Mantas Mazeika
Basel Alomair
Jacob Steinhardt
    ELMRALM
ArXiv (abs)PDFHTMLHuggingFace (3 upvotes)

Papers citing "Measuring Massive Multitask Language Understanding"

6 / 4,456 papers shown
Title
Scaling Laws for Transfer
Scaling Laws for Transfer
Danny Hernandez
Jared Kaplan
T. Henighan
Sam McCandlish
356
282
0
02 Feb 2021
What Makes Good In-Context Examples for GPT-$3$?
What Makes Good In-Context Examples for GPT-333?Workshop on Knowledge Extraction and Integration for Deep Learning Architectures; Deep Learning Inside Out (DEELIO), 2021
Jiachang Liu
Dinghan Shen
Yizhe Zhang
Bill Dolan
Lawrence Carin
Weizhu Chen
AAMLRALM
550
1,591
0
17 Jan 2021
How Can We Know When Language Models Know? On the Calibration of
  Language Models for Question Answering
How Can We Know When Language Models Know? On the Calibration of Language Models for Question AnsweringTransactions of the Association for Computational Linguistics (TACL), 2020
Zhengbao Jiang
Jun Araki
Haibo Ding
Graham Neubig
UQCV
381
505
0
02 Dec 2020
Language Models are Open Knowledge Graphs
Language Models are Open Knowledge Graphs
Chenguang Wang
Xiao Liu
Basel Alomair
SSLKELM
171
144
0
22 Oct 2020
Limitations of Autoregressive Models and Their Alternatives
Limitations of Autoregressive Models and Their Alternatives
Chu-cheng Lin
Aaron Jaech
Xin Li
Matthew R. Gormley
Jason Eisner
146
73
0
22 Oct 2020
Current Limitations of Language Models: What You Need is Retrieval
Current Limitations of Language Models: What You Need is Retrieval
Aran Komatsuzaki
LRM
93
3
0
15 Sep 2020
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