可解释的NLP的合理化:一项调查调查
Sai Gurrapu1, Ajay Kulkarni2, Lifu Huang1
1Department of Computer Science, Virginia Tech, Blacksburg, VA, United States.
Frontiers in artificial intelligence
|October 11, 2023
概括
自然语言处理 (NLP) 中可解释的AI (人工智能) 正在推进合理化,提供人类可以理解的解释. 这项调查组织了现场,并为未来的研究引入了理性AI (RAI).
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.
- 可解释的人工智能
背景情况:
- 在NLP任务中深度学习的进步提高了性能,但降低了模型的可解释性.
- 黑盒模型阻碍了对系统内部和决策过程的理解.
- 现有的可解释性方法 (例如,LIME,Shapley,突出热图) 需要专业知识,而且不够.
研究的目的:
- 提供2007年至2022年NLP理性化文献的第一个全面调查.
- 在NLP合理化中组织和分析现有的方法,评估,代码和数据集.
- 引入理性AI (RAI) 作为可解释AI的新子领域,以推进合理化.
主要方法:
- 在自然语言处理中的合理化技术的系统文献综述.
- 分析方法,评估指标,可用的代码和数据集.
- 研究的分类和趋势和差距的识别.
主要成果:
- 合理化为模型输出提供直观,人类可以理解的自然语言解释 (理性解释).
- 在NLP的合理化领域目前是无组织的.
- 确定了用于合理化研究的关键方法,评估策略和资源.
结论:
- 合理化是一种可供非技术用户使用的可解释性技术.
- 该调查为新兴的理性AI (RAI) 领域奠定了基础.
- 讨论了NLP合理化的未来研究方向和机会.
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