CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
<|reference_start|>CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search: 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.<|reference_end|>
@article{mo2024chiq:,
    title={CHIQ: Contextual History Enhancement for Improving Query Rewriting in
  Conversational Search},
    author={Fengran Mo, Abbas Ghaddar, Kelong Mao, Mehdi Rezagholizadeh, Boxing
  Chen, Qun Liu, Jian-Yun Nie},
    journal={arXiv preprint arXiv:2406.05013},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05013},
    primaryClass={cs.IR cs.CL}
}