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相关实验视频

Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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通过一致性校准来塑造预先训练的语言模型,用于特定任务的嵌入生成.

Jianqi Gao1, Hang Yu2, Yiu-Ming Cheung3

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.

Neural networks : the official journal of the International Neural Network Society
|June 25, 2025
PubMed
概括

EGO-PLM通过使用它们作为嵌入式生成器来增强预训练语言模型 (PLM). 这种新的方法使微调与预培训任务保持一致,防止知识被遗忘并改善下游绩效.

关键词:
一致性校准 (CoCa) 的方法预先训练有素的语言模型特定任务的嵌入式生成器特定任务的微调.

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 预先训练的语言模型 (PLM) 对于下游任务至关重要,但在微调过程中可能会忘记预先训练的知识.
  • 这种知识遗忘限制了在特定任务上的表现.

研究的目的:

  • 引入EGO-PLM,这是一种使用PLM作为特定任务的嵌入式生成器的新方法.
  • 将微调任务与预训练任务协调一致,以减轻知识遗忘.

主要方法:

  • EGO-PLM采用一种任务不可知的预定义任务,类似于预训练.
  • 一个特定任务的嵌入式生成器将PLM调整为下游任务,与预定义的任务一起进行训练.
  • 一致性校准 (CoCa) 使用对抗性培训来调整预定义和特定任务的目标,解决冲突并确保特定任务的嵌入.

主要成果:

  • 在8个数据集和6个任务类别中,EGO-PLM表现出一致和实质性的改进.
  • 该方法在微调性能方面表现优于最先进的基线.

结论:

  • EGO-PLM有效地解决了在微调过程中在PLM中遗忘知识的挑战.
  • 拟议的方法通过利用PLM作为特定任务的嵌入式生成器来提高下游任务性能.