In this study, we introduce Direct Preference Optimization (DPO) as a powerful technique for preference learning and model evaluation, validated through extensive experiments.
Our experimental setup leverages GPT-4 to assess the win rates of different summarization treatments, where responses are randomly ordered to mitigate bias.
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