  Retrieval-augmented generation (RAG) has received much attention for
Open-domain question-answering (ODQA) tasks as a means to compensate for the
parametric knowledge of large language models (LLMs). While previous approaches
focused on processing retrieved passages to remove irrelevant context, they
still rely heavily on the quality of retrieved passages which can degrade if
the question is ambiguous or complex. In this paper, we propose a simple yet
efficient method called question and passage augmentation via LLMs for
open-domain QA. Our method first decomposes the original questions into
multiple-step sub-questions. By augmenting the original question with detailed
sub-questions and planning, we are able to make the query more specific on what
needs to be retrieved, improving the retrieval performance. In addition, to
compensate for the case where the retrieved passages contain distracting
information or divided opinions, we augment the retrieved passages with
self-generated passages by LLMs to guide the answer extraction. Experimental
results show that the proposed scheme outperforms the previous state-of-the-art
and achieves significant performance gain over existing RAG methods.
