Interactions with billion-scale large language models typically yield long-form responses due to their extensive parametric capacities, along with retrieval-augmented features. While detailed responses provide insightful viewpoint of a specific subject, they frequently generate redundant and less engaging content that does not meet user interests. In this work, we focus on the role of query outlining (i.e., selected sequence of queries) in scenarios that users request a specific range of information, namely coverage-conditioned ($C^2$) scenarios. For simulating $C^2$ scenarios, we construct QTree, 10K sets of information-seeking queries decomposed with various perspectives on certain topics. By utilizing QTree, we train QPlanner, a 7B language model generating customized query outlines that follow coverage-conditioned queries. We analyze the effectiveness of generated outlines through automatic and human evaluation, targeting on retrieval-augmented generation (RAG). Moreover, the experimental results demonstrate that QPlanner with alignment training can further provide outlines satisfying diverse user interests. Our resources are available at https://github.com/youngerous/qtree.

对于与大规模语言模型的交互通常会产生长篇回复，这是由于其庞大的参数容量以及带有检索增强功能。本研究聚焦于查询规划在特定信息范围请求的场景下的作用，通过构建QTree并使用QPlanner，分析生成的查询规划的有效性，并证明具有对齐训练的QPlanner能够满足多样的用户兴趣。

学习探索和选择以实现覆盖编码的检索增强生成