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Language Models can Evaluate Themselves via Probability Discrepancy

Language Models can Evaluate Themselves via Probability Discrepancy

17 May 2024
Tingyu Xia
Bowen Yu
Yuan Wu
Yi-Ju Chang
Chang Zhou
    ELM
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Papers citing "Language Models can Evaluate Themselves via Probability Discrepancy"

4 / 4 papers shown
Title
Large Language Models are Superpositions of All Characters: Attaining
  Arbitrary Role-play via Self-Alignment
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Keming Lu
Bowen Yu
Chang Zhou
Jingren Zhou
37
56
0
23 Jan 2024
WARM: On the Benefits of Weight Averaged Reward Models
WARM: On the Benefits of Weight Averaged Reward Models
Alexandre Ramé
Nino Vieillard
Léonard Hussenot
Robert Dadashi
Geoffrey Cideron
Olivier Bachem
Johan Ferret
95
92
0
22 Jan 2024
Training language models to follow instructions with human feedback
Training language models to follow instructions with human feedback
Long Ouyang
Jeff Wu
Xu Jiang
Diogo Almeida
Carroll L. Wainwright
...
Amanda Askell
Peter Welinder
Paul Christiano
Jan Leike
Ryan J. Lowe
OSLM
ALM
301
11,730
0
04 Mar 2022
Teaching Machines to Read and Comprehend
Teaching Machines to Read and Comprehend
Karl Moritz Hermann
Tomás Kociský
Edward Grefenstette
L. Espeholt
W. Kay
Mustafa Suleyman
Phil Blunsom
170
3,504
0
10 Jun 2015
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