CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究

Hongyu Li; Zhihui Yang; Renfen Hu (胡韧奋)

CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究

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Abstract

"FIE2025任务旨在使用大语言模型对文本及相关假设进行叙实性推理。我们参加了微调和非微调两个赛道,分别在人工数据集和自然数据集上采用提示词优化和词表RAG策略融合语言学知识,并利用模型集成投票方法提升判断准确率。评测结果显示,我们的方法在非微调赛道取得了0.9351的成绩,在微调赛道取得了0.9261的成绩,均位列第三名。"

Anthology ID:: 2025.ccl-2.19
Volume:: Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
Month:: August
Year:: 2025
Address:: Jinan, China
Editors:: Hongfei Lin, Bin Li, Hongye Tan
Venue:: CCL
SIG:
Publisher:: Chinese Information Processing Society of China
Note:
Pages:: 157–165
Language:
URL:: https://aclanthology.org/2025.ccl-2.19/
DOI:
Bibkey:
Cite (ACL):: Hongyu Li, Zhihui Yang, and Renfen Hu. 2025. CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 157–165, Jinan, China. Chinese Information Processing Society of China.
Cite (Informal):: CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究 (Li et al., CCL 2025)
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PDF:: https://aclanthology.org/2025.ccl-2.19.pdf

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@inproceedings{li-etal-2025-ccl25-evalren,
    title = "{CCL}25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究",
    author = "Li, Hongyu  and
      Yang, Zhihui  and
      Hu, Renfen",
    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-2.19/",
    pages = "157--165",
    abstract = "``FIE2025任务旨在使用大语言模型对文本及相关假设进行叙实性推理。我们参加了微调和非微调两个赛道,分别在人工数据集和自然数据集上采用提示词优化和词表RAG策略融合语言学知识,并利用模型集成投票方法提升判断准确率。评测结果显示,我们的方法在非微调赛道取得了0.9351的成绩,在微调赛道取得了0.9261的成绩,均位列第三名。''"
}

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%0 Conference Proceedings
%T CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究
%A Li, Hongyu
%A Yang, Zhihui
%A Hu, Renfen
%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 li-etal-2025-ccl25-evalren
%X “FIE2025任务旨在使用大语言模型对文本及相关假设进行叙实性推理。我们参加了微调和非微调两个赛道,分别在人工数据集和自然数据集上采用提示词优化和词表RAG策略融合语言学知识,并利用模型集成投票方法提升判断准确率。评测结果显示,我们的方法在非微调赛道取得了0.9351的成绩,在微调赛道取得了0.9261的成绩,均位列第三名。”
%U https://aclanthology.org/2025.ccl-2.19/
%P 157-165

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Markdown (Informal)

[CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究](https://aclanthology.org/2025.ccl-2.19/) (Li et al., CCL 2025)

CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究 (Li et al., CCL 2025)

ACL

Hongyu Li, Zhihui Yang, and Renfen Hu. 2025. CCL25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 157–165, Jinan, China. Chinese Information Processing Society of China.