用轨迹优化提升问答效率，让AI更懂用户真实需求。
TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference
- 通过轨迹优化生成精准提问序列，避免无效对话。
- 在标准任务上比基线方法提升9.32%准确率。
- 适合需要深度交互与偏好挖掘的应用场景。
大语言模型可通过多轮对话有效获取人类偏好。复杂任务可由模型作为提问者（STaR-GATE；Andukuri et al., 2024）通过迭代澄清问题和最终回应完成。然而，现有基于自教推理的方法难以识别最优对话路径，常产生与任务无关的问题。为此，我们提出TO-GATE，一个新框架，通过轨迹优化增强问题生成，包含两个核心组件：澄清解析器生成最优提问路径，摘要器确保任务对齐的最终回应。轨迹优化使模型能生成针对特定任务的有效提问与总结回应。实验表明，TO-GATE显著优于基线方法，在标准偏好获取任务上提升9.32%。
原文摘要 · Abstract (English)
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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