@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 = "Zhou, Yuhang and
Li, Zeping and
Tian, Siyu and
Ni, Yuchen and
Zhang, Jian and
Liu, Xiang and
Ye, Guangnan and
Wu, Jie and
Chai, Hongfeng",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
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 = "``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。''"
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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 Zhou, Yuhang
%A Li, Zeping
%A Tian, Siyu
%A Ni, Yuchen
%A Zhang, Jian
%A Liu, Xiang
%A Ye, Guangnan
%A Wu, Jie
%A Chai, Hongfeng
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%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/) (Zhou et al., CCL 2024)
ACL
- Yuhang Zhou, Zeping Li, Siyu Tian, Yuchen Ni, Jian Zhang, Xiang Liu, Guangnan Ye, Jie Wu, and Hongfeng Chai. 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.