@inproceedings{qi-etal-2024-ji,
title = "基于动态聚类与标签空间映射的上下文学习模板构建方法(In-Context Learning Demonstration Construction Method based on Dynamic Clustering and Label Space Mapping)",
author = "Qi, Zhang and
Xingnan, Jin and
Yu, Pei and
Yongping, Du",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.69/",
pages = "883--893",
language = "zho",
abstract = "{\textquotedblleft}面向大语言模型提供自然语言指令,可生成预期输出,体现了其上下文学习能力。上下文学习的性能与上下文模板质量密切相关,现有的工作通常使用单一的选择算法进行模板构建,无法充分激发上下文学习能力。本文提出基于动态聚类与标签空间映射的上下文学习模板构建方法,动态选择相关示例,进一步提出聚类筛选方法,实现不同语义簇中示例多样化的选择。设计基于损失函数的排序选择方法,评估模板学习正确标签空间映射分布的能力,排序形成最终模板。在自然语言推理等任务中的实验结果表明,本文提出的方法使两个不同的大语言模型准确率最高分别提升3.2{\%}和8.9{\%}。{\textquotedblright}"
}
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<abstract>“面向大语言模型提供自然语言指令,可生成预期输出,体现了其上下文学习能力。上下文学习的性能与上下文模板质量密切相关,现有的工作通常使用单一的选择算法进行模板构建,无法充分激发上下文学习能力。本文提出基于动态聚类与标签空间映射的上下文学习模板构建方法,动态选择相关示例,进一步提出聚类筛选方法,实现不同语义簇中示例多样化的选择。设计基于损失函数的排序选择方法,评估模板学习正确标签空间映射分布的能力,排序形成最终模板。在自然语言推理等任务中的实验结果表明,本文提出的方法使两个不同的大语言模型准确率最高分别提升3.2%和8.9%。”</abstract>
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%0 Conference Proceedings
%T 基于动态聚类与标签空间映射的上下文学习模板构建方法(In-Context Learning Demonstration Construction Method based on Dynamic Clustering and Label Space Mapping)
%A Qi, Zhang
%A Xingnan, Jin
%A Yu, Pei
%A Yongping, Du
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F qi-etal-2024-ji
%X “面向大语言模型提供自然语言指令,可生成预期输出,体现了其上下文学习能力。上下文学习的性能与上下文模板质量密切相关,现有的工作通常使用单一的选择算法进行模板构建,无法充分激发上下文学习能力。本文提出基于动态聚类与标签空间映射的上下文学习模板构建方法,动态选择相关示例,进一步提出聚类筛选方法,实现不同语义簇中示例多样化的选择。设计基于损失函数的排序选择方法,评估模板学习正确标签空间映射分布的能力,排序形成最终模板。在自然语言推理等任务中的实验结果表明,本文提出的方法使两个不同的大语言模型准确率最高分别提升3.2%和8.9%。”
%U https://aclanthology.org/2024.ccl-1.69/
%P 883-893
Markdown (Informal)
[基于动态聚类与标签空间映射的上下文学习模板构建方法(In-Context Learning Demonstration Construction Method based on Dynamic Clustering and Label Space Mapping)](https://aclanthology.org/2024.ccl-1.69/) (Qi et al., CCL 2024)
ACL