Papers with model-free monolithic odds ratio preference optimization algorithm

1 papers
ORPO: Monolithic Preference Optimization without Reference Model (2024.emnlp-main)

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Challenge: Pre-trained language models with vast training corpora have shown remarkable abilities in diverse natural language processing tasks.
Approach: They propose a model-free monolithic odds ratio preference optimization algorithm, ORPO, to improve preference alignment.
Outcome: The proposed algorithm outperforms state-of-the-art language models with more than 7B and 13B parameters on the ultrafeedback alone.

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