TO-GATE applies trajectory-level direct preference optimization with a weighted response loss to improve preference elicitation dialogues, claiming 83.15% win rate versus 73.83% for STaR-GATE.
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TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference
TO-GATE applies trajectory-level direct preference optimization with a weighted response loss to improve preference elicitation dialogues, claiming 83.15% win rate versus 73.83% for STaR-GATE.