欺骗问题答案模型:一种混合的词级对抗方法
Jiyao Li1, Mingze Ni1, Yongshun Gong2
1University of Technology Sydney, 15 Broadway, Sydney, 2007, NSW, Australia.
概括
这项研究介绍了QA-Attack,这是一个新的对抗策略,用于愚弄问答 (QA) 模型. 单词级攻击有效地欺骗了质量保证系统,在稳定性测试中超过了现有的方法.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 机器学习 (ML) 是指机器学习.
背景情况:
- 深度学习赋予了先进的NLP任务,如问答 (QA).
- 质量保证模型对抗敌对攻击的稳定性是一个关键的,未被充分研究的问题.
研究的目的:
- 介绍QA-Attack,一个新的词级对抗策略.
- 评估这一策略在欺骗质量保证模型方面的有效性.
主要方法:
- 一个基于注意力的攻击,利用定制的注意力机制.
- 删除排名策略用于识别和准特定单词.
- 同义词替换以创建欺骗性的输入,同时保持语法.
主要成果:
- 在各种问题类型中,QA-Attack成功地欺骗了基线QA模型.
- 展示了多功能性,特别是在长文本输入.
- 在成功率,语义变化,BLEU分数,流利性和语法错误率方面优于现有的对抗技术.
结论:
- 质量保证攻击 (QA-Attack) 是一种多功能且有效的策略,用于评估质量保证模型的稳定性.
- 强调当前质量保证模型对复杂的对抗性攻击的脆弱性.
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