Using Captum to Explain Generative Language Models
Vivek Miglani Aobo Yang Aram H. Markosyan Diego Garcia-Olano Narine Kokhlikyan
Captum is a comprehensive library for model explainability in PyTorch,
offering a range of methods from the interpretability literature to enhance
users' understanding of PyTorch models. In this paper, we introduce new
features in Captum that are specifically designed to analyze the behavior of
generative language models. We provide an overview of the available
functionalities and example applications of their potential for understanding
learned associations within generative language models.