@inproceedings{shitu-etal-2024-ji,
title = "基于大型语言模型的中文空间语义评测",
author = "Shitu, Huo and
Yujun, Wang and
Tongjie, Wu",
editor = "Lin, Hongfei and
Tan, Hongye and
Li, Bin",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-3.11/",
pages = "95--105",
language = "zho",
abstract = "{\textquotedblleft}本研究的任务旨在让大模型进行实体识别、角色识别、异常识别、信息推理、同义识别任务,综合评估大模型的空间语义理解能力。其中,我们使用普通提示词、工作流提示词和思维链三种提示词策略来探讨大模型的空间语义理解能力,最后发现ERNIE-4在1-shot的普通提示词上表现最佳。最终,我们的方法排名第六,总体准确率得分为56.20{\%}。{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shitu-etal-2024-ji">
<titleInfo>
<title>基于大型语言模型的中文空间语义评测</title>
</titleInfo>
<name type="personal">
<namePart type="given">Huo</namePart>
<namePart type="family">Shitu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wang</namePart>
<namePart type="family">Yujun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wu</namePart>
<namePart type="family">Tongjie</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 3: Evaluations)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hongfei</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hongye</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bin</namePart>
<namePart type="family">Li</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>“本研究的任务旨在让大模型进行实体识别、角色识别、异常识别、信息推理、同义识别任务,综合评估大模型的空间语义理解能力。其中,我们使用普通提示词、工作流提示词和思维链三种提示词策略来探讨大模型的空间语义理解能力,最后发现ERNIE-4在1-shot的普通提示词上表现最佳。最终,我们的方法排名第六,总体准确率得分为56.20%。”</abstract>
<identifier type="citekey">shitu-etal-2024-ji</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-3.11/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>95</start>
<end>105</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T 基于大型语言模型的中文空间语义评测
%A Shitu, Huo
%A Yujun, Wang
%A Tongjie, Wu
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F shitu-etal-2024-ji
%X “本研究的任务旨在让大模型进行实体识别、角色识别、异常识别、信息推理、同义识别任务,综合评估大模型的空间语义理解能力。其中,我们使用普通提示词、工作流提示词和思维链三种提示词策略来探讨大模型的空间语义理解能力,最后发现ERNIE-4在1-shot的普通提示词上表现最佳。最终,我们的方法排名第六,总体准确率得分为56.20%。”
%U https://aclanthology.org/2024.ccl-3.11/
%P 95-105
Markdown (Informal)
[基于大型语言模型的中文空间语义评测](https://aclanthology.org/2024.ccl-3.11/) (Shitu et al., CCL 2024)
ACL
- Huo Shitu, Wang Yujun, and Wu Tongjie. 2024. 基于大型语言模型的中文空间语义评测. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 95–105, Taiyuan, China. Chinese Information Processing Society of China.