Recent developments in transformer-based language models have allowed them to
capture a wide variety of world knowledge that can be adapted to downstream
tasks with limited resources. However, what pieces of information are
understood in these models is unclear, and neuron-level contributions in
identifying them are largely unknown. Conventional approaches in neuron
explainability either depend on a finite set of pre-defined descriptors or
require manual annotations for training a secondary model that can then explain
the neurons of the primary model. In this paper, we take BERT as an example and
we try to remove these constraints and propose a novel and scalable framework
that ties textual descriptions to neurons. We leverage the potential of
generative language models to discover human-interpretable descriptors present
in a dataset and use an unsupervised approach to explain neurons with these
descriptors. Through various qualitative and quantitative analyses, we
demonstrate the effectiveness of this framework in generating useful
data-specific descriptors with little human involvement in identifying the
neurons that encode these descriptors. In particular, our experiment shows that
the proposed approach achieves 75% precision@2, and 50% recall@2

使用 BERT 为例，本文提出了一种新颖且可扩展的框架，将文本描述与神经元联系起来，并通过生成式语言模型发现数据特定的可解释描述符，用于解释编码这些描述符的神经元。实验证明，该方法在识别神经元方面达到了 75% 的 2 阶精度和 50% 的 2 阶回想率。