CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search

• URL: http://arxiv.org/abs/2406.05013v2
• Date: Thu, 26 Sep 2024 06:19:34 GMT
• Title: CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
• Authors: Fengran Mo, Abbas Ghaddar, Kelong Mao, Mehdi Rezagholizadeh, Boxing Chen, Qun Liu, Jian-Yun Nie,
• Abstract summary: We introduce CHIQ, a two-step method that leverages the capabilities of open-source large language models (LLMs) to resolve ambiguities in the conversation history before query rewriting. We demonstrate on five well-established benchmarks that CHIQ leads to state-of-the-art results across most settings.
• Score: 67.6104548484555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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