对预训练的大型模型进行对抗性域调整中的混平衡微调
Shuoran Jiang1, Qingcai Chen2, Yang Xiang3
1Haibin Institute of Technology, ShenZhen, Harbin Institute of Technology campus, Taoyuan Street, Nanshan District, Shenzhen, 518055, GuangDong, China.
概括
本研究介绍了CadaFT,这是一种用于对抗域适应 (ADA) 的新方法,可以有效地平衡预训练的大型模型 (PLM) 中的混因素. CadaFT通过减轻偏见来增强跨领域的知识转移,优于现有的ADA技术.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 预训练的大型模型 (PLM) 在概括和上下文学习方面表现出色,使他们非常适合对抗性领域适应 (ADA).
- 现有的ADA方法与混器作斗争,这些混器导致源域和目标域之间的分布差异.
- 混变量引入了偏见,限制了PLM中知识从源到目标领域的有效转移.
研究的目的:
- 为PLM微调 (CadaFT) 提出一个新的混平衡方法,用于对抗领域的适应.
- 解决当前ADA方法在处理混因素方面的局限性.
- 改进域不变表示学习,消除PLM中的混者偏见.
主要方法:
- 使用PLM作为特征提取的基础,加上域分类器和混分类器.
- 采用联合训练的对抗损失,通过减少域分类器歧视来增强域不变表示学习.
- 在培训期间,平衡会混源和未测量域之间的分布.
主要成果:
- CadaFT正确地识别了域不变特征中的混因素,有效地消除了PLM提取特征中的偏差.
- 与包括GPT-4,LLaMA2和ViT.在内的最先进的ADA方法相比,拟议的方法显示出更高的性能.
- 对自然语言处理和计算机视觉任务的实证验证证证了CadaFT的有效性.
结论:
- CadaFT通过明确处理混变量,为对抗领域的适应提供了一个强大的解决方案.
- 插即用混分类器适应各种测量场景 (可测量,不可测量,部分可测量).
- CadaFT显著推进了PLM的域调整领域,提高了它们在各种现实场景中的适用性.
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