解释基础语言模型的变化语言概念
Hengyi Wang1, Shiwei Tan1, Zhqing Hong1
1Department of Computer Science, Rutgers University.
概括
基础语言模型 (FLM) 提供先进的自然语言处理,但缺乏直观的解释. 我们的新VALC框架提供了概念层面的见解,增强了理解,超出了简单的词汇分析.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习的可解释性
- 人工智能的人工智能
背景情况:
- 像BERT这样的基础语言模型 (FLM) 在NLP任务中表现出色.
- 目前使用注意力权重的可解释性方法只提供单词级别的洞察力.
- 文字层次的解释缺乏更高层次的结构,阻碍了可读性和直觉.
研究的目的:
- 正式定义和引入"概念解释"的概念.
- 开发一个新的框架,在FLM中实现概念层面的可解释性.
- 克服现有的文字层次解释技术的局限性.
主要方法:
- 提出了一个名为VAriational Language Concept (VALC) 的变量贝叶斯框架.
- 开发了理论分析,以证明VALC在寻找语言概念方面的最佳性.
- 在各种现实数据集上验证了框架.
主要成果:
- VALC成功地超越了单词级别的解释,提供了概念级别的见解.
- 理论分析证实,VALC确定了解释FLM预测的最佳概念.
- 经验结果证明了该框架在多个数据集中的有效性.
结论:
- 该VALC框架在FLM解释性方面取得了重大进展.
- 概念解释为模型预测提供了更直观和可读的解释.
- 这种方法提高了基础语言模型的可靠性和可用性.
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