  Retrieval-augmented generation (RAG) effectively addresses issues of static
knowledge and hallucination in large language models. Existing studies mostly
focus on question scenarios with clear user intents and concise answers.
However, it is prevalent that users issue broad, open-ended queries with
diverse sub-intents, for which they desire rich and long-form answers covering
multiple relevant aspects. To tackle this important yet underexplored problem,
we propose a novel RAG framework, namely RichRAG. It includes a sub-aspect
explorer to identify potential sub-aspects of input questions, a multi-faceted
retriever to build a candidate pool of diverse external documents related to
these sub-aspects, and a generative list-wise ranker, which is a key module to
provide the top-k most valuable documents for the final generator. These ranked
documents sufficiently cover various query aspects and are aware of the
generator's preferences, hence incentivizing it to produce rich and
comprehensive responses for users. The training of our ranker involves a
supervised fine-tuning stage to ensure the basic coverage of documents, and a
reinforcement learning stage to align downstream LLM's preferences to the
ranking of documents. Experimental results on two publicly available datasets
prove that our framework effectively and efficiently provides comprehensive and
satisfying responses to users.
