@inproceedings{bohan-etal-2024-ji,
title = "基于指令微调与数据增强的儿童故事常识推理与寓意理解研究",
author = "Bohan, Yu and
Yunlong, Li and
Tao, Liu and
Aoze, Zheng and
Kunli, Zhang and
Hongying, Zan",
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-3.36/",
pages = "320--326",
language = "zho",
abstract = "{\textquotedblleft}尽管现有语言模型在自然语言处理任务上表现出色,但在深层次语义理解和常识推理方面仍有提升空间。本研究通过测试模型在儿童故事常识推理与寓意理解数据集(CRMUS)上的性能,探究如何增强模型在复杂任务中的能力。在本次任务的赛道二中,本研究使用多个7B以内的开源大模型(如Qwen、InternLM等)进行零样本推理,并选择表现最优的模型基于LoRA进行指令微调来提高其表现。除此之外,本研究还对数据集进行了分析与增强。研究结果显示,通过设计有效的指令格式和调整LoRA微调参数,模型在常识推理和寓意理解上的准确率显著提高。最终在本次任务的赛道二中取得第一名的成绩,该任务的评价指标Acc值为74.38,达到了较为先进的水准。{\textquotedblright}"
}
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<abstract>“尽管现有语言模型在自然语言处理任务上表现出色,但在深层次语义理解和常识推理方面仍有提升空间。本研究通过测试模型在儿童故事常识推理与寓意理解数据集(CRMUS)上的性能,探究如何增强模型在复杂任务中的能力。在本次任务的赛道二中,本研究使用多个7B以内的开源大模型(如Qwen、InternLM等)进行零样本推理,并选择表现最优的模型基于LoRA进行指令微调来提高其表现。除此之外,本研究还对数据集进行了分析与增强。研究结果显示,通过设计有效的指令格式和调整LoRA微调参数,模型在常识推理和寓意理解上的准确率显著提高。最终在本次任务的赛道二中取得第一名的成绩,该任务的评价指标Acc值为74.38,达到了较为先进的水准。”</abstract>
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%0 Conference Proceedings
%T 基于指令微调与数据增强的儿童故事常识推理与寓意理解研究
%A Bohan, Yu
%A Yunlong, Li
%A Tao, Liu
%A Aoze, Zheng
%A Kunli, Zhang
%A Hongying, Zan
%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 bohan-etal-2024-ji
%X “尽管现有语言模型在自然语言处理任务上表现出色,但在深层次语义理解和常识推理方面仍有提升空间。本研究通过测试模型在儿童故事常识推理与寓意理解数据集(CRMUS)上的性能,探究如何增强模型在复杂任务中的能力。在本次任务的赛道二中,本研究使用多个7B以内的开源大模型(如Qwen、InternLM等)进行零样本推理,并选择表现最优的模型基于LoRA进行指令微调来提高其表现。除此之外,本研究还对数据集进行了分析与增强。研究结果显示,通过设计有效的指令格式和调整LoRA微调参数,模型在常识推理和寓意理解上的准确率显著提高。最终在本次任务的赛道二中取得第一名的成绩,该任务的评价指标Acc值为74.38,达到了较为先进的水准。”
%U https://aclanthology.org/2024.ccl-3.36/
%P 320-326
Markdown (Informal)
[基于指令微调与数据增强的儿童故事常识推理与寓意理解研究](https://aclanthology.org/2024.ccl-3.36/) (Bohan et al., CCL 2024)
ACL
- Yu Bohan, Li Yunlong, Liu Tao, Zheng Aoze, Zhang Kunli, and Zan Hongying. 2024. 基于指令微调与数据增强的儿童故事常识推理与寓意理解研究. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 320–326, Taiyuan, China. Chinese Information Processing Society of China.