Advances in language modeling have led to the development of deep\nattention-based models that are performant across a wide variety of natural\nlanguage processing (NLP) problems. These language models are typified by a\npre-training process on large unlabeled text corpora and subsequently\nfine-tuned for specific tasks. Although considerable work has been devoted to\nunderstanding the attention mechanisms of pre-trained models, it is less\nunderstood how a model's attention mechanisms change when trained for a target\nNLP task. In this paper, we propose a visual analytics approach to\nunderstanding fine-tuning in attention-based language models. Our\nvisualization, Attention Flows, is designed to support users in querying,\ntracing, and comparing attention within layers, across layers, and amongst\nattention heads in Transformer-based language models. To help users gain\ninsight on how a classification decision is made, our design is centered on\ndepicting classification-based attention at the deepest layer and how attention\nfrom prior layers flows throughout words in the input. Attention Flows supports\nthe analysis of a single model, as well as the visual comparison between\npre-trained and fine-tuned models via their similarities and differences. We\nuse Attention Flows to study attention mechanisms in various sentence\nunderstanding tasks and highlight how attention evolves to address the nuances\nof solving these tasks.\n