广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (5): 59-71.doi: 10.16088/j.issn.1001-6600.2022030802

• 综述 • 上一篇    下一篇

预训练语言模型的可解释性研究进展

郝雅茹1, 董力1, 许可2*, 李先贤3   

  1. 1.微软亚洲研究院, 北京 100191;
    2.北京航空航天大学 计算机学院, 北京 100083;
    3.广西多源信息挖掘与安全重点实验室(广西师范大学), 广西 桂林 541004
  • 收稿日期:2022-03-08 修回日期:2022-05-05 出版日期:2022-09-25 发布日期:2022-10-18
  • 通讯作者: 许可(1971—), 男, 重庆人, 北京航空航天大学教授, 博士, 博导。E-mail: kexu@buaa.edu.cn
  • 基金资助:
    国家自然科学基金重点项目(61932002)

Interpretability of Pre-trained Language Models: A Survey

HAO Yaru1, DONG Li1, XU Ke2*, LI Xianxian3   

  1. 1. Microsoft Research Asia, Beijing 100191, China;
    2. School of Computer Science and Engineering, Beihang University, Beijing 100083, China;
    3. Guangxi Key Laboratory of Multi-Source Information Mining and Security (Guangxi Normal University), Guilin Guangxi 541004, China
  • Received:2022-03-08 Revised:2022-05-05 Online:2022-09-25 Published:2022-10-18

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126

摘要： 基于深度神经网络的大型预训练语言模型在众多自然语言处理任务上都取得了巨大的成功,如文本分类、阅读理解、机器翻译等,目前已经广泛应用于工业界。然而,这些模型的可解释性普遍较差,即难以理解为何特定的模型结构和预训练方式如此有效,亦无法解释模型做出决策的内在机制,这给人工智能模型的通用化带来不确定性和不可控性。因此,设计合理的方法来解释模型至关重要,它不仅有助于分析模型的行为,也可以指导研究者更好地改进模型。本文介绍近年来有关大型预训练语言模型可解释性的研究现状,对相关文献进行综述,并分析现有方法的不足和未来可能的发展方向。

关键词: 语言模型, 预训练, 可解释性, 自然语言处理, 神经网络

Abstract: Large-scale pre-trained language models based on deep neural networks have achieved great success in various natural language processing tasks, such as text classification, reading comprehension, machine translation, etc., and have been widely used in the industry. However, the interpretability of these models is generally poor, that is, it is difficult for us to understand the reasons why different model structures and pre-training methods are effective, and to explain the internal mechanism of the models making predictions, which brings difficulties to the generalization of artificial intelligence models because of the uncertainty and the uncontrollability. Therefore, it is crucial to design reasonable methods to explain the model, which can not only effectively explain the behavior of the model, but also guide researchers to better improve the model. This paper introduces various research statuses of the interpretability of large-scale pre-trained language models in recent years, reviews related methods, and analyzes the shortcomings of the existing methods and possible future research directions.

Key words: language model, pre-training, interpretability, natural language processing, neural networks

中图分类号: 

  • TP391.1
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