Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. However, traditional CQG, focusing primarily on the immediate context, lacks the conversational foresight necessary to guide conversations toward specified conclusions. This limitation significantly restricts their ability to achieve conclusion-oriented conversational outcomes. In this work, we redefine the CQG task as Conclusion-driven Conversational Question Generation (CCQG) by focusing on proactivity, not merely reacting to the unfolding conversation but actively steering it towards a conclusion-oriented question-answer pair. To address this, we propose a novel approach, called Proactive Conversational Question Planning with self-Refining (PCQPR). Concretely, by integrating a planning algorithm inspired by Monte Carlo Tree Search (MCTS) with the analytical capabilities of large language models (LLMs), PCQPR predicts future conversation turns and continuously refines its questioning strategies. This iterative self-refining mechanism ensures the generation of contextually relevant questions strategically devised to reach a specified outcome. Our extensive evaluations demonstrate that PCQPR significantly surpasses existing CQG methods, marking a paradigm shift towards conclusion-oriented conversational question-answering systems.

本研究针对传统对话问题生成（CQG）在引导对话达成特定结论方面的局限，提出了一种以结果为导向的对话问题生成（CCQG）方法。通过将蒙特卡洛树搜索（MCTS）算法与大型语言模型（LLMs）相结合，提出了主动对话问题规划与自我反思（PCQPR），有效预测未来对话并优化问题策略。研究结果表明，PCQPR在实现目标导向型对话方面显著优于现有方法，具有重要的应用潜力。 

主动对话问题规划与反思