面向目标导向对话中高效信息获取的反馈感知蒙特卡洛树搜索
Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations
AI总结：
提出一种结合LLM和MCTS的反馈感知框架，通过分层反馈机制利用历史交互模式指导提问策略，在医疗和故障排除任务中显著提升成功率并大幅减少LLM调用次数。
摘要：
对话系统中有效的决策和问题解决，需要具备通过针对性提问来识别和获取缺失信息的能力。一个关键挑战在于，如何通过提出最小化不确定性的问题，来高效缩小庞大的可能结果空间。为解决这一问题，我们引入了一个新颖的框架，该框架利用大型语言模型（LLMs）生成信息获取问题，并利用蒙特卡洛树搜索（MCTS）作为推理时规划的一部分，战略性地选择能最大化信息增益的问题。我们的主要贡献包括一种分层反馈机制，该机制利用过去的交互模式来指导未来的策略。具体而言，每个新问题都会基于语义相似度被映射到一个簇，并且我们的UCT（树的上置信界）公式采用特定簇的奖励，以优先选择过去对类似问题证明有效的成功提问轨迹。在医疗诊断和技术故障排除领域的广泛实证评估表明，与现有技术相比，我们的方法在成功率上平均提高了12%，并且每次对话中用于规划的LLM调用次数减少了约10倍。当我们从一组受限的可能性开始时，成功率平均额外获得8%的提升。我们的结果强调了反馈感知MCTS在增强目标导向对话信息获取方面的有效性。
英文摘要：
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select questions that maximize information gain, as a part of inference-time planning. Our primary contribution includes a hierarchical feedback mechanism that exploits past interaction patterns to guide future strategy. Specifically, each new problem is mapped to a cluster based on semantic similarity, and our UCT (Upper Confidence bound for Trees) formulation employs a cluster-specific bonus reward to prioritize successful question trajectories that have proven effective for similar problems in the past. Extensive empirical evaluation across medical diagnosis and technical troubleshooting domains shows that our method achieves an average of 12% improvement in success rates and about 10x reduction in the number of LLM calls made for planning per conversation, compared to the state of the art. An additional 8% gain in success rate is observed on average when we start with a constrained set of possibilities. Our results underscore the efficacy of feedback-aware MCTS in enhancing information-seeking in goal-oriented dialogues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
