@inproceedings{mo-etal-2024-chiq,
title = "{CHIQ}: Contextual History Enhancement for Improving Query Rewriting in Conversational Search",
author = "Mo, Fengran and
Ghaddar, Abbas and
Mao, Kelong and
Rezagholizadeh, Mehdi and
Chen, Boxing and
Liu, Qun and
Nie, Jian-Yun",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.135",
pages = "2253--2268",
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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%0 Conference Proceedings
%T CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
%A Mo, Fengran
%A Ghaddar, Abbas
%A Mao, Kelong
%A Rezagholizadeh, Mehdi
%A Chen, Boxing
%A Liu, Qun
%A Nie, Jian-Yun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F mo-etal-2024-chiq
%X 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.
%U https://aclanthology.org/2024.emnlp-main.135
%P 2253-2268
Markdown (Informal)
[CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search](https://aclanthology.org/2024.emnlp-main.135) (Mo et al., EMNLP 2024)
ACL
- Fengran Mo, Abbas Ghaddar, Kelong Mao, Mehdi Rezagholizadeh, Boxing Chen, Qun Liu, and Jian-Yun Nie. 2024. CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 2253–2268, Miami, Florida, USA. Association for Computational Linguistics.