@inproceedings{ao-etal-2024-ji,
title = "基于{C}hat{GPT}查询改写的文档检索方法(Document Retrieval Method Based on {C}hat{GPT} Query Rewriting)",
author = "Ao, Li and
Xinhui, Tu and
Yinghao, Xiong",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.2/",
pages = "15--26",
language = "zho",
abstract = "{\textquotedblleft}查询改写是一种通过优化查询从而提高检索结果质量的技术。传统的基于伪相关反馈的方法受限于伪相关文档的质量。本文提出了一种基于ChatGPT查询改写的文档检索方法。这种方法不依赖伪相关文档,可以避免伪相关文档质量不高的问题。首先,利用BM25模型进行检索,获得初次检索结果集;同时借助ChatGPT生成新查询;然后分别将原始查询和新查询作为输入,利用重排模型对初次检索结果集进行重排,得到各自的文档相关性得分;最后,将两个查询的文档相关性得分进行融合,得到最终的文档得分。在多个检索测试集上的实验结果表明,相比于基准模型,基于ChatGPT查询改写的文档检索方法在nDCG@10指标上平均提升了约4.5个百分点。{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ao-etal-2024-ji">
<titleInfo>
<title>基于ChatGPT查询改写的文档检索方法(Document Retrieval Method Based on ChatGPT Query Rewriting)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Li</namePart>
<namePart type="family">Ao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tu</namePart>
<namePart type="family">Xinhui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiong</namePart>
<namePart type="family">Yinghao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">zho</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maosong</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiye</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianpei</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiyuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“查询改写是一种通过优化查询从而提高检索结果质量的技术。传统的基于伪相关反馈的方法受限于伪相关文档的质量。本文提出了一种基于ChatGPT查询改写的文档检索方法。这种方法不依赖伪相关文档,可以避免伪相关文档质量不高的问题。首先,利用BM25模型进行检索,获得初次检索结果集;同时借助ChatGPT生成新查询;然后分别将原始查询和新查询作为输入,利用重排模型对初次检索结果集进行重排,得到各自的文档相关性得分;最后,将两个查询的文档相关性得分进行融合,得到最终的文档得分。在多个检索测试集上的实验结果表明,相比于基准模型,基于ChatGPT查询改写的文档检索方法在nDCG@10指标上平均提升了约4.5个百分点。”</abstract>
<identifier type="citekey">ao-etal-2024-ji</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.2/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>15</start>
<end>26</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T 基于ChatGPT查询改写的文档检索方法(Document Retrieval Method Based on ChatGPT Query Rewriting)
%A Ao, Li
%A Xinhui, Tu
%A Yinghao, Xiong
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F ao-etal-2024-ji
%X “查询改写是一种通过优化查询从而提高检索结果质量的技术。传统的基于伪相关反馈的方法受限于伪相关文档的质量。本文提出了一种基于ChatGPT查询改写的文档检索方法。这种方法不依赖伪相关文档,可以避免伪相关文档质量不高的问题。首先,利用BM25模型进行检索,获得初次检索结果集;同时借助ChatGPT生成新查询;然后分别将原始查询和新查询作为输入,利用重排模型对初次检索结果集进行重排,得到各自的文档相关性得分;最后,将两个查询的文档相关性得分进行融合,得到最终的文档得分。在多个检索测试集上的实验结果表明,相比于基准模型,基于ChatGPT查询改写的文档检索方法在nDCG@10指标上平均提升了约4.5个百分点。”
%U https://aclanthology.org/2024.ccl-1.2/
%P 15-26
Markdown (Informal)
[基于ChatGPT查询改写的文档检索方法(Document Retrieval Method Based on ChatGPT Query Rewriting)](https://aclanthology.org/2024.ccl-1.2/) (Ao et al., CCL 2024)
ACL