@inproceedings{hongyan-etal-2024-ji,
title = "基于上下文学习的空间语义理解",
author = "Hongyan, Wu and
Nankai, Lin and
Peijian, Ceng and
Weixiong, Zheng and
Shengyi, Jiang and
Aimin, Yang",
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-3.13/",
pages = "113--121",
language = "zho",
abstract = "{\textquotedblleft}空间语义理解任务致力于使语言模型能够准确解析和理解文本中描述的物体间的空间方位关系,这一能力对于深入理解自然语言并支持复杂的空间推理至关重要。本文聚焦于探索大模型的上下文学习策略在空间语义理解任务上的有效性,提出了一种基于选项相似度与空间语义理解能力相似度的样本选择策略。本文将上下文学习与高效微调融合对开源模型进行微调,以提高大模型的空间语义理解能力。此外,本文尝试结合开源模型和闭源模型的能力处理不同类型的样本。实验结果显示,本文所采用的策略有效地提高了大模型在空间语义理解任务上的性能。{\textquotedblright}"
}
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<abstract>“空间语义理解任务致力于使语言模型能够准确解析和理解文本中描述的物体间的空间方位关系,这一能力对于深入理解自然语言并支持复杂的空间推理至关重要。本文聚焦于探索大模型的上下文学习策略在空间语义理解任务上的有效性,提出了一种基于选项相似度与空间语义理解能力相似度的样本选择策略。本文将上下文学习与高效微调融合对开源模型进行微调,以提高大模型的空间语义理解能力。此外,本文尝试结合开源模型和闭源模型的能力处理不同类型的样本。实验结果显示,本文所采用的策略有效地提高了大模型在空间语义理解任务上的性能。”</abstract>
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%0 Conference Proceedings
%T 基于上下文学习的空间语义理解
%A Hongyan, Wu
%A Nankai, Lin
%A Peijian, Ceng
%A Weixiong, Zheng
%A Shengyi, Jiang
%A Aimin, Yang
%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 hongyan-etal-2024-ji
%X “空间语义理解任务致力于使语言模型能够准确解析和理解文本中描述的物体间的空间方位关系,这一能力对于深入理解自然语言并支持复杂的空间推理至关重要。本文聚焦于探索大模型的上下文学习策略在空间语义理解任务上的有效性,提出了一种基于选项相似度与空间语义理解能力相似度的样本选择策略。本文将上下文学习与高效微调融合对开源模型进行微调,以提高大模型的空间语义理解能力。此外,本文尝试结合开源模型和闭源模型的能力处理不同类型的样本。实验结果显示,本文所采用的策略有效地提高了大模型在空间语义理解任务上的性能。”
%U https://aclanthology.org/2024.ccl-3.13/
%P 113-121
Markdown (Informal)
[基于上下文学习的空间语义理解](https://aclanthology.org/2024.ccl-3.13/) (Hongyan et al., CCL 2024)
ACL
- Wu Hongyan, Lin Nankai, Ceng Peijian, Zheng Weixiong, Jiang Shengyi, and Yang Aimin. 2024. 基于上下文学习的空间语义理解. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 113–121, Taiyuan, China. Chinese Information Processing Society of China.