The Thought Preference Optimization (TPO) approach enhances the response quality of instruction-fine-tuned large language models (LLMs) by allowing them to generate structured internal thoughts.
Traditional models focus solely on final answers, but TPO's modified Chain-of-Thought reasoning method encourages models to think before responding, leading to better accuracy and coherence.
Through an iterative training process, the TPO method uses generated thoughts, evaluated by a judge model, to create chosen and rejected pairs that improve response relevance.
By adjusting training prompts to promote internal thinking, TPO allows language models to refine their answers based on effectiveness, without exposing their intermediate thought steps to users.
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