Document detail
ID
oai:arXiv.org:2312.05491
Topic
Computer Science - Computation and... Computer Science - Artificial Inte... I.2.7Author
Miglani, Vivek Yang, Aobo Markosyan, Aram H. Garcia-Olano, Diego Kokhlikyan, NarineCategory
Computer Science
Year
2023
listing date
12/13/2023
Keywords
generative languageAbstract
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.
Miglani, Vivek,Yang, Aobo,Markosyan, Aram H.,Garcia-Olano, Diego,Kokhlikyan, Narine, 2023, Using Captum to Explain Generative Language Models
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
instance-level isahp learning granger causality
