PCQPR : Proactive Conversational Question Planning with Reflection (2024)

Abstract: 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.

## introduction conversational question generation (cqg) has significantly enhanced the capabilities of conversational question-answering (qa) systems, bringing a level of dynamism and intelligence that was previously unattainable. in various fields such as educational technology, customer service, and interactive what were his duties on that committee? a federal aviation administration task group on future air navigation and air traffic control systems. what else interesting happened with him in aviation? alvarez served on multiple high level committees related to civilian and military aviation. what were some of his careers that involved flying? during world war ii he led the development of multiple aviation-related technologies. entertainment, cqg has become a crucial component for enhancing user interactions, enabling more natural and responsive conversations between humans and machines. by generating contextually appropriate questions, cqg systems have improved the interactive experience, making conversations with ai more engaging and informative. despite these advancements, current cqg methods are predominantly reactive, generating questions based solely on the immediate context without strategically considering the specified conversational outcome. this reactivity is evident in various existing approaches: from choosing the most rel-evant conversational history to enhance question relevance @xcite , to utilizing a contextenhanced neural model for identifying key contexts @xcite , and designing a two-stage framework for generating more natural conversations @xcite . while these methods contribute significantly to the field, their inherent reactivity limits their potential to direct conversations toward specific, predetermined conclusions. these identified limitations underscore the necessity for a paradigm shift towards proactiveness in cqg, leading us to propose the novel task of conclusion-driven conversational question generation (ccqg), as illustrated in figure 1 . ccqg emphasizes the strategic generation of questions to guide conversations toward specific outcomes, a critical capability that reactive models fail to support adequately. by focusing on proactive question generation, we aim to overcome the limitations of reactivity and enable more purposeful and outcomeoriented question-answering conversations. in contrast to conversational qa systems, the field of proactive dialogue systems has recently seen the emergence of several frameworks designed to accurately predict user needs and steer conversations accordingly. methods such as the application of mcts for dialogue planning @xcite @xcite , and the use of large language models (llms) for predicting conversation trajectories @xcite @xcite , represent significant strides towards proactive dialogue management. however, these approaches do not directly address the nuanced requirements of ccqg, which relies on strategically generating questions to guide conversations toward specified outcomes. to bridge this gap, we propose the proactive conversational question planning with self-refining (pcqpr) framework, which innovatively integrates strategic planning and reflective refinement. by incorporating an mcts-like planning algorithm with the analytical capabilities of llms, our approach utilizes llms to predict and simulate future conversation paths, thus guiding the question generation toward the specified, desired outcome. concurrently, llms engage in reflective analysis of these simulations, providing critical feedback for each action within the simulation paths to inform and enhance conversational strategies. this reflective analysis identifies failures and showcases successful experiences, providing a balanced and thorough evaluation of each planning path. by adopting the comparable reflection mechanism that captures errors and successes, the framework guarantees the systematic incorporation of insights, leading to progressively enhanced question generation. the pc-qpr framework's innovative use of llms for both forward-looking simulation and reflective feedback marks a strategic shift from reactive to proactive cqg, aligning conversations more effectively with specified outcomes. ## conclusion we present a novel task setting, ccqg, designed to generate subsequent questions that proactively guide conversations toward the specified outcome. to address this task, we propose an innovative framework, pcqpr, which uniquely combines the mcts-like planning algorithm with llms to enhance planning capabilities. this approach conducts a lookahead search to explore multiple potential paths. furthermore, we introduce a novel reflection mechanism that provides insightful verbal feedback for each action along the entire planning path. extensive experiments demonstrate the superior performance of pcqpr over closed-source and open-source llms. we believe this effort could be inspiring for future research in ai-driven conversational question-answering systems.