学习博览会的代表性,用于微调预先训练的语言模型
Ke Wang1, Yinghao Zhang1, Hong-Yu Zhang1
1College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.
概括
这项研究介绍了CFPLM,这是一个用于退化预训练语言模型 (PLM) 的新框架. 在不损害性能的情况下,CFPLM使用因果推理来减少AI语言模型中的社会偏见.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 预训练语言模型 (PLM) 在各种NLP任务中表现出色,但继承了人类的偏见.
- 这些偏见,包括社会刻板印象,限制了PLM的安全和道德应用.
- 现有的调解方法往往不能有效地解决偏见的根本原因.
研究的目的:
- 为预先训练的语言模型提出一个新的 debiasing 框架,CFPLM.
- 利用因果推理来识别和干预PLM中诱导偏见的因素.
- 提高PLM的公平性,同时保持他们的语言理解能力.
主要方法:
- 开发了CFPLM (预先训练的语言模型的因果框架) 调试框架.
- 整合了一个复合损失函数,并包含一个公平性罚款条款.
- 集成的对抗性损失和调节,以优化性能.
主要成果:
- 在BERT,RoBERTa和ALBERT等流行的PLM中,CFPLM显著减少了偏差.
- 对标准数据集和指标的评估证实了退化方法的有效性.
- 在GLUE基准指标上的表现显示,在语言理解能力方面没有妥协.
结论:
- 拟议的CFPLM框架有效地使用因果推理来缓解PLM中的偏见.
- 通过CFPLM提高公平性不会对模型的核心语言理解能力产生负面影响.
- CFPLM为开发更道德和可靠的AI语言技术提供了一个有希望的方向.
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