  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.
