Large Language Models (LLMs) play a powerful \textit{Reader} of the
\textit{Retrieve-then-Read} pipeline, making great progress in knowledge-based
open-domain tasks. This work introduces a new framework,
\textit{Rewrite-Retrieve-Read} that improves the retrieval-augmented method
from the perspective of the query rewriting. Prior studies mostly contribute to
adapt the retriever or stimulate the reader. Different from them, our approach
pay attention of the query adaptation. Because the original query can not be
always optimal to retrieve for the LLM, especially in the real world.(1) We
first prompt an LLM to rewrite the queries, then conduct retrieval-augmented
reading. (2) We further apply a small language model as a trainable rewriter,
which rewrite the search query to cater to the frozen retriever and the LLM
reader. To fine-tune the rewriter, we first use a pseudo data to conduct
supervised warm-up training. Then the \textit{Retrieve-then-Read} pipeline is
modeled as a reinforcement learning context. The rewriter is further trained as
a policy model by maximize the reward of the pipeline performance. Evaluation
is performed on two downstream tasks, open-domain QA and multiple choice. Our
framework is proved effective and scalable.

该研究介绍了一个新的 Rewrite-Retrieve-Read 框架，该框架从查询重写的角度改进了检索增强方法，并通过强化学习调整该框架的策略模型，通过开放域 QA 等下游任务进行了验证。