  Conversational search seeks to retrieve relevant passages for the given
questions in Conversational QA (ConvQA). Questions in ConvQA face challenges
such as omissions and coreferences, making it difficult to obtain desired
search results. Conversational Query Reformulation (CQR) transforms these
current queries into de-contextualized forms to resolve these issues. However,
existing CQR methods focus on rewriting human-friendly queries, which may not
always yield optimal search results for the retriever. To overcome this
challenge, we introduce GuideCQR, a framework that utilizes guided documents to
refine queries, ensuring that they are optimal for retrievers. Specifically, we
augment keywords, generate expected answers from the re-ranked documents, and
unify them with the filtering process. Experimental results show that queries
enhanced by guided documents outperform previous CQR methods. Especially,
GuideCQR surpasses the performance of Large Language Model (LLM) prompt-powered
approaches and demonstrates the importance of the guided documents in
formulating retriever-friendly queries across diverse setups.
