CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913519051472896 |
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| author | Mo, Fengran Ghaddar, Abbas Mao, Kelong Rezagholizadeh, Mehdi Chen, Boxing Liu, Qun Nie, Jian-Yun |
| author_facet | Mo, Fengran Ghaddar, Abbas Mao, Kelong Rezagholizadeh, Mehdi Chen, Boxing Liu, Qun Nie, Jian-Yun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search Mo, Fengran Ghaddar, Abbas Mao, Kelong Rezagholizadeh, Mehdi Chen, Boxing Liu, Qun Nie, Jian-Yun Information Retrieval Computation and Language 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. |
| title | CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2406.05013 |