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Recent self-rewarding large language models (LLM) have successfully applied LLM-as-a-Judge to iteratively improve the alignment performance without the need of human annotations for preference data.
The proof and measurement of association between two things
C Spearman · 1904
Earlier work this paper cites.
A new measure of rank correlation
Maurice G Kendall · 1938
Earlier work this paper cites.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
Earlier work this paper cites.
Kendall rank correlation and mann-kendall trend test
A Ian McLeod · 2005
Earlier work this paper cites.
Model compression
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Earlier work this paper cites.
The kendall rank correlation coefficient
Hervé Abdi · 2007
Earlier work this paper cites.
Influence functions of the spearman and kendall correlation measures
Christophe Croux and Catherine Dehon · 2010
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Distilling the knowledge in a neural network
Geoffrey Hinton · 2015
Earlier work this paper cites.
Deep reinforcement learning from human preferences
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Earlier work this paper cites.
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Confidence regularized self-training
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
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Foundational challenges in assuring alignment and safety of large language models
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