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KTO: Model Alignment as Prospect Theoretic Optimization Kawin Ethayarajh 1 Winnie Xu 2 Niklas Muennighoff 2 Dan Jurafsky 1 Douwe Kiela 1 2 Abstract Kahneman & Tversky’sprospect theorytells us that humans perceive random variables in a biased but well-defined manner (1992); for example, hu-
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Author:
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe Kiela
CreationDate:
2024-02-05T01:46:03+00:00
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LaTeX with hyperref
Keywords:
Machine Learning, ICML
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2024-02-05T01:46:03+00:00
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This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5
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Proceedings of the International Conference on Machine Learning 2024
Title:
KTO: Model Alignment as Prospect Theoretic Optimization
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18
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