  In this paper, we study how open-source large language models (LLMs) can be
effectively deployed for improving query rewriting in conversational search,
especially for ambiguous queries. We introduce CHIQ, a two-step method that
leverages the capabilities of LLMs to resolve ambiguities in the conversation
history before query rewriting. This approach contrasts with prior studies that
predominantly use closed-source LLMs to directly generate search queries from
conversation history. We demonstrate on five well-established benchmarks that
CHIQ leads to state-of-the-art results across most settings, showing highly
competitive performances with systems leveraging closed-source LLMs. Our study
provides a first step towards leveraging open-source LLMs in conversational
search, as a competitive alternative to the prevailing reliance on commercial
LLMs. Data, models, and source code will be publicly available upon acceptance
at https://github.com/fengranMark/CHIQ.
