Visual Analytics for Generative Transformer Models
Raymond Li Ruixin Yang Wen Xiao Ahmed AbuRaed Gabriel Murray Giuseppe Carenini
While transformer-based models have achieved state-of-the-art results in a
variety of classification and generation tasks, their black-box nature makes
them challenging for interpretability. In this work, we present a novel visual
analytical framework to support the analysis of transformer-based generative
networks. In contrast to previous work, which has mainly focused on
encoder-based models, our framework is one of the first dedicated to supporting
the analysis of transformer-based encoder-decoder models and decoder-only
models for generative and classification tasks. Hence, we offer an intuitive
overview that allows the user to explore different facets of the model through
interactive visualization. To demonstrate the feasibility and usefulness of our
framework, we present three detailed case studies based on real-world NLP
research problems.