@inproceedings{yuhang-etal-2024-yin,
title = "银瞳:基于自适应语义空间学习的中文金融多任务大模型({S}ilver{S}ight: A Multi-Task {C}hinese Financial Large Language Model Based on Adaptive Semantic Space Learning)",
author = "Yuhang, Zhou and
Zeping, Li and
Siyu, Tian and
Yuchen, Ni and
Jian, Zhang and
Xiang, Liu and
Guangnan, Ye and
Jie, Wu and
Hongfeng, Chai",
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-1.74/",
pages = "955--972",
language = "zho",
abstract = "{\textquotedblleft}大语言模型正逐渐被用于各种垂直领域,利用其广泛的知识储备来赋能领域中的多种场景。然而,各领域拥有多种待学习的特定任务,且多源异构的领域数据容易引发模型进行任务迁移时的冲突。基于此,本研究提出自适应语义空间学习框架,利用对语义空间内数据的自适应重分布,提升多专家模型的性能及选择效果,并基于此框架训练了一个金融多任务大模型{\textquotedblleft}银瞳{\textquotedblright}。研究结果表明,我们的框架只需利用10{\%}的数据就能达到接近全数据训练的效果,并拥有较强的泛化表现。{\textquotedblright}"
}
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<title>银瞳:基于自适应语义空间学习的中文金融多任务大模型(SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning)</title>
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<abstract>“大语言模型正逐渐被用于各种垂直领域,利用其广泛的知识储备来赋能领域中的多种场景。然而,各领域拥有多种待学习的特定任务,且多源异构的领域数据容易引发模型进行任务迁移时的冲突。基于此,本研究提出自适应语义空间学习框架,利用对语义空间内数据的自适应重分布,提升多专家模型的性能及选择效果,并基于此框架训练了一个金融多任务大模型“银瞳”。研究结果表明,我们的框架只需利用10%的数据就能达到接近全数据训练的效果,并拥有较强的泛化表现。”</abstract>
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%0 Conference Proceedings
%T 银瞳:基于自适应语义空间学习的中文金融多任务大模型(SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning)
%A Yuhang, Zhou
%A Zeping, Li
%A Siyu, Tian
%A Yuchen, Ni
%A Jian, Zhang
%A Xiang, Liu
%A Guangnan, Ye
%A Jie, Wu
%A Hongfeng, Chai
%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 yuhang-etal-2024-yin
%X “大语言模型正逐渐被用于各种垂直领域,利用其广泛的知识储备来赋能领域中的多种场景。然而,各领域拥有多种待学习的特定任务,且多源异构的领域数据容易引发模型进行任务迁移时的冲突。基于此,本研究提出自适应语义空间学习框架,利用对语义空间内数据的自适应重分布,提升多专家模型的性能及选择效果,并基于此框架训练了一个金融多任务大模型“银瞳”。研究结果表明,我们的框架只需利用10%的数据就能达到接近全数据训练的效果,并拥有较强的泛化表现。”
%U https://aclanthology.org/2024.ccl-1.74/
%P 955-972
Markdown (Informal)
[银瞳:基于自适应语义空间学习的中文金融多任务大模型(SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning)](https://aclanthology.org/2024.ccl-1.74/) (Yuhang et al., CCL 2024)
ACL
- Zhou Yuhang, Li Zeping, Tian Siyu, Ni Yuchen, Zhang Jian, Liu Xiang, Ye Guangnan, Wu Jie, and Chai Hongfeng. 2024. 银瞳:基于自适应语义空间学习的中文金融多任务大模型(SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning). In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 955–972, Taiyuan, China. Chinese Information Processing Society of China.