TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference

• URL: http://arxiv.org/abs/2506.02827v1
• Date: Tue, 03 Jun 2025 12:58:07 GMT
• Title: TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference
• Authors: Yulin Dou, Jiangming Liu,
• Abstract summary: Large language models (LLMs) can effectively elicit human preferences through multi-turn dialogue.<n>Existing approaches based on self-taught reasoning struggle to identify optimal dialogue trajectories.<n>We propose TO-GATE, a novel framework that enhances question generation through trajectory optimization.
• Score: 3.8396210019383306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) can effectively elicit human preferences through multi-turn dialogue. Complex tasks can be accomplished through iterative clarifying questions and final responses generated by an LLM acting as a questioner (STaR-GATE; Andukuri et al., 2024}). However, existing approaches based on self-taught reasoning struggle to identify optimal dialogue trajectories and avoid irrelevant questions to the tasks. To address this limitation, we propose TO-GATE, a novel framework that enhances question generation through trajectory optimization, which consists of two key components: a clarification resolver that generates optimal questioning trajectories, and a summarizer that ensures task-aligned final responses. The trajectory optimization enables the model to produce effective elicitation questions and summary responses tailored to specific tasks. Experimental results demonstrate that TO-GATE significantly outperforms baseline methods, achieving a 9.32% improvement on standard preference elicitation tasks.

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