用强化学习让对话系统学会问关键问题，高效精准找信息
Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrieval
- 用强化学习训练代理，自动生成二元提问序列缩小搜索范围
- 结构化任务接近理论最优，非结构化任务显著优于基线
- 无需大量标注数据，适合需要高效交互检索的场景
信息检索中的用户查询常含歧义，单次查询难以确定用户真实意图。现有对话式交互检索系统虽能澄清意图，但因缺乏明确的提问策略而效率低下。为此，我们提出 SherlockLLM，一种基于对话的检索框架，通过强化学习学习最优提问策略，无需大规模标注对话数据。该框架训练一个智能体生成一系列二元问题，以高效缩小搜索空间。为验证方法，我们构建了一个包含结构化与非结构化任务的基准测试。实验结果表明，SherlockLLM 在结构化任务上表现媲美强基线，接近二分查找定义的理论最优；在更具挑战性的非结构化任务中，其性能显著超越基线，展现出学习高效信息获取对话策略的能力。
原文摘要 · Abstract (English)
User queries in information retrieval are often ambiguous, making it challenging for systems to identify a user's target from a single query. While recent dialogue-based interactive retrieval systems can clarify user intent, they are inefficient as they often lack an explicit strategy to ask the most informative questions. To address this limitation, we propose SherlockLLM, a dialogue-driven retrieval framework that learns an optimal questioning strategy via Reinforcement Learning (RL) and avoids the need for large-scale annotated dialogue data. In our framework, an agent is trained to generate a sequence of binary questions to efficiently narrow down the search space. To validate our approach, we introduce a benchmark with both structured and unstructured tasks. Experimental results show that SherlockLLM is a robust and efficient solution. On the structured tasks, its performance matches strong baselines and approaches the theoretical optimal defined by binary search. On the challenging unstructured task, our agent significantly outperforms these baselines, showcasing its ability to learn a highly effective information-seeking dialogue policy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。