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Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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一种基于自适应融合的数据增强方法,用于抽象对话总结.

Weihao Li1, Dan Jiang1, Han Zhang1

  • 1School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China.

PeerJ. Computer science
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了自适应增强融合 (AAF),以改善用有限数据进行对话总结模型训练. AAF提高了模型性能和概括性,在基准数据集上表现优于其他方法.

关键词:
抽象对话总结 抽象对话总结适应性聚变增强技术数据增强数据增强

相关实验视频

Last Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

693

科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能

背景情况:

  • 对话总结对于信息检索至关重要.
  • 训练抽象对话总结模型需要大量的标记数据.
  • 手动总结是昂贵和耗时的,阻碍了模型开发.

研究的目的:

  • 为了应对对话总结的注释数据不足的挑战.
  • 提出一种新的数据增强方法,即自适应增强融合 (AAF).
  • 在资源有限的环境中平衡模型学习效率和概括能力.

主要方法:

  • 集成的轻微扰动增强 (MPA) 和语义重建增强 (SRA) 进入AAF.
  • 开发了用于对话总结的数据增强策略.
  • 在DialogSum和SAMSum数据集上评估了AAF方法.

主要成果:

  • 在资源有限的条件下,AAF显著提高了ROUGE分数.
  • 拟议的方法表现优于基线方法.
  • 增强数据的数量极大地影响了模型培训的结果.

结论:

  • AAF是一种有效的数据增强技术,用于对话总结.
  • 该方法为具有有限注释数据的培训模型提供了可行的解决方案.
  • 代码是公开可用的可复制性和进一步研究.