@inproceedings{wang-2025-ccl25,
title = "{CCL}25-Eval 任务5系统报告:基于千问大模型的古诗词理解与推理研究",
author = "Wang, Jue",
editor = "Lin, Hongfei and
Li, Bin and
Tan, Hongye",
booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
month = aug,
year = "2025",
address = "Jinan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2025.ccl-2.25/",
pages = "206--211",
abstract = "``中国古典诗词语言凝练、意境深远,对自然语言处理系统提出了严峻挑战。本次评测聚焦于古诗词理解与推理,包括词语释义、句子翻译和情感分析三项子任务。本文基于Qwen2.5-14B-Instruct 模型,在LLaMA Factory 框架下采用监督微调(SFT)与LoRA 参数高效微调策略,提升模型在few-shot 条件下的表现。训练数据来自官方发布的多类别JSON 格式语料,经整合与指令格式转换后用于模型训练。实验表明,LoRA 微调显著优于zero-shot 基线。本研究验证了参数高效微调方法在有限数据场景下的有效性。''"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-2025-ccl25">
<titleInfo>
<title>CCL25-Eval 任务5系统报告:基于千问大模型的古诗词理解与推理研究</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jue</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)</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">Bin</namePart>
<namePart type="family">Li</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>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Jinan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“中国古典诗词语言凝练、意境深远,对自然语言处理系统提出了严峻挑战。本次评测聚焦于古诗词理解与推理,包括词语释义、句子翻译和情感分析三项子任务。本文基于Qwen2.5-14B-Instruct 模型,在LLaMA Factory 框架下采用监督微调(SFT)与LoRA 参数高效微调策略,提升模型在few-shot 条件下的表现。训练数据来自官方发布的多类别JSON 格式语料,经整合与指令格式转换后用于模型训练。实验表明,LoRA 微调显著优于zero-shot 基线。本研究验证了参数高效微调方法在有限数据场景下的有效性。”</abstract>
<identifier type="citekey">wang-2025-ccl25</identifier>
<location>
<url>https://aclanthology.org/2025.ccl-2.25/</url>
</location>
<part>
<date>2025-08</date>
<extent unit="page">
<start>206</start>
<end>211</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T CCL25-Eval 任务5系统报告:基于千问大模型的古诗词理解与推理研究
%A Wang, Jue
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F wang-2025-ccl25
%X “中国古典诗词语言凝练、意境深远,对自然语言处理系统提出了严峻挑战。本次评测聚焦于古诗词理解与推理,包括词语释义、句子翻译和情感分析三项子任务。本文基于Qwen2.5-14B-Instruct 模型,在LLaMA Factory 框架下采用监督微调(SFT)与LoRA 参数高效微调策略,提升模型在few-shot 条件下的表现。训练数据来自官方发布的多类别JSON 格式语料,经整合与指令格式转换后用于模型训练。实验表明,LoRA 微调显著优于zero-shot 基线。本研究验证了参数高效微调方法在有限数据场景下的有效性。”
%U https://aclanthology.org/2025.ccl-2.25/
%P 206-211
Markdown (Informal)
[CCL25-Eval 任务5系统报告:基于千问大模型的古诗词理解与推理研究](https://aclanthology.org/2025.ccl-2.25/) (Wang, CCL 2025)
ACL
- Jue Wang. 2025. CCL25-Eval 任务5系统报告:基于千问大模型的古诗词理解与推理研究. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 206–211, Jinan, China. Chinese Information Processing Society of China.