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        <title>CCL25-Eval任务四系统报告:宏观模式提示与高效微调在叙实性推理中的应用</title>
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    <name type="personal">
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        <namePart type="given">Yuanhao</namePart>
        <namePart type="family">Zhong</namePart>
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        <namePart type="family">Chai</namePart>
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            <title>Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)</title>
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            <namePart type="given">Hongfei</namePart>
            <namePart type="family">Lin</namePart>
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            <namePart type="given">Bin</namePart>
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        <name type="personal">
            <namePart type="given">Hongye</namePart>
            <namePart type="family">Tan</namePart>
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    <abstract>“本文研究了利用大语言模型进行谓词引导的叙实性推理任务。在不微调场景下,针对Gemini 2.5 Pro模型,我们构建了基于谓词类型的思维链(CoT)提示,并创新性地让模型学习整个带答案的样本集以归纳宏观模式和规则,最终形成高效的提示词模板。在微调场景下,我们选用Qwen3-32b模型,利用llama factory进行LoRA微调,并使用llama.cpp完成模型向gguf格式的转换、量化及Ollama部署。实验结果展示了所提方法的有效性,其中在不微调赛道上,基于宏观模式提示的方法取得了94.01%的准确率;在微调赛道上,基于微调模型的系统取得了92.61%的准确率。”</abstract>
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%0 Conference Proceedings
%T CCL25-Eval任务四系统报告:宏观模式提示与高效微调在叙实性推理中的应用
%A Li, Zequn
%A Zhong, Yuanhao
%A Chai, Chengliang
%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
%X “本文研究了利用大语言模型进行谓词引导的叙实性推理任务。在不微调场景下,针对Gemini 2.5 Pro模型,我们构建了基于谓词类型的思维链(CoT)提示,并创新性地让模型学习整个带答案的样本集以归纳宏观模式和规则,最终形成高效的提示词模板。在微调场景下,我们选用Qwen3-32b模型,利用llama factory进行LoRA微调,并使用llama.cpp完成模型向gguf格式的转换、量化及Ollama部署。实验结果展示了所提方法的有效性,其中在不微调赛道上,基于宏观模式提示的方法取得了94.01%的准确率;在微调赛道上,基于微调模型的系统取得了92.61%的准确率。”
%U https://aclanthology.org/2025.ccl-2.14/
%P 118-127

Markdown (Informal)

[CCL25-Eval任务四系统报告:宏观模式提示与高效微调在叙实性推理中的应用](https://aclanthology.org/2025.ccl-2.14/) (Li et al., CCL 2025)

• CCL25-Eval任务四系统报告:宏观模式提示与高效微调在叙实性推理中的应用 (Li et al., CCL 2025)

ACL

• Zequn Li, Yuanhao Zhong, and Chengliang Chai. 2025. CCL25-Eval任务四系统报告:宏观模式提示与高效微调在叙实性推理中的应用. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 118–127, Jinan, China. Chinese Information Processing Society of China.