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Recent advances in interpretability suggest we can project weights and hiddenstates of transformer-based language models (LMs) to their vocabulary, atransformation that makes them more human interpretable. In this paper, weinvestigate LM attention heads and memory values, the vectors the modelsdynamically create and recall while processing a given input. By analyzing thetokens they represent through this projection, we identify patterns in theinformation flow inside the attention mechanism. Based on our discoveries, wecreate a tool to visualize a forward pass of Generative Pre-trainedTransformers (GPTs) as an interactive flow graph, with nodes representingneurons or hidden states and edges representing the interactions between them.Our visualization simplifies huge amounts of data into easy-to-read plots thatcan reflect the models' internal processing, uncovering the contribution ofeach component to the models' final prediction. Our visualization also unveilsnew insights about the role of layer norms as semantic filters that influencethe models' output, and about neurons that are always activated during forwardpasses and act as regularization vectors.

VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers