  Open-domain question answering (ODQA) has emerged as a pivotal research
spotlight in information systems. Existing methods follow two main paradigms to
collect evidence: (1) The \textit{retrieve-then-read} paradigm retrieves
pertinent documents from an external corpus; and (2) the
\textit{generate-then-read} paradigm employs large language models (LLMs) to
generate relevant documents. However, neither can fully address multifaceted
requirements for evidence. To this end, we propose LLMQA, a generalized
framework that formulates the ODQA process into three basic steps: query
expansion, document selection, and answer generation, combining the superiority
of both retrieval-based and generation-based evidence. Since LLMs exhibit their
excellent capabilities to accomplish various tasks, we instruct LLMs to play
multiple roles as generators, rerankers, and evaluators within our framework,
integrating them to collaborate in the ODQA process. Furthermore, we introduce
a novel prompt optimization algorithm to refine role-playing prompts and steer
LLMs to produce higher-quality evidence and answers. Extensive experimental
results on widely used benchmarks (NQ, WebQ, and TriviaQA) demonstrate that
LLMQA achieves the best performance in terms of both answer accuracy and
evidence quality, showcasing its potential for advancing ODQA research and
applications.
