title: AttentionViz: A Global View of Transformer Attention
summary: Transformer models are revolutionizing machine learning, but their inner
  workings remain mysterious. In this work, we present a new visualization
  technique designed to help researchers understand the self-attention mechanism
  in transformers that allows these models to learn rich, contextual
  relationships between elements of a sequence. The main idea behind our method
  is to visualize a joint embedding of the query and key vectors used by
  transformer models to compute attention. Unlike previous attention
  visualization techniques, our approach enables the analysis of global patterns
  across multiple input sequences. We create an interactive visualization tool,
  AttentionViz, based on these joint query-key embeddings, and use it to study
  attention mechanisms in both language and vision transformers. We demonstrate
  the utility of our approach in improving model understanding and offering new
  insights about query-key interactions through several application scenarios and
  expert feedback.
paper_authors._id: 64584c5f32ac1687a28c792d
paper_authors.name: Catherine Yeh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-09T08:52:16.921Z
paper_authors._id: 6312606695407887cb78af89
paper_authors.avatarUrl: /avatars/55d2ec0736ae91dc0c07c99bc418528f.svg
paper_authors.fullname: Catherine Yeh
paper_authors.type: user
paper_authors.user: catherinesyeh
paper_authors._id: 64584c5f32ac1687a28c792e
paper_authors.name: Yida Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-08T09:28:58.957Z
paper_authors._id: 6312618c5e2531edb9ee9fb0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6312618c5e2531edb9ee9fb0/CxFdN-z8pTn8qCLcJ16bT.png
paper_authors.fullname: Yida Chen
paper_authors.type: user
paper_authors.user: YidaChen
paper_authors._id: 64584c5f32ac1687a28c792f
paper_authors.name: Aoyu Wu
paper_authors._id: 64584c5f32ac1687a28c7930
paper_authors.name: Cynthia Chen
paper_authors._id: 64584c5f32ac1687a28c7931
paper_authors.name: Fernanda Viégas
paper_authors._id: 64584c5f32ac1687a28c7932
paper_authors.name: Martin Wattenberg
paper_title: AttentionViz: A Global View of Transformer Attention
paper_summary: Transformer models are revolutionizing machine learning, but their inner
  workings remain mysterious. In this work, we present a new visualization
  technique designed to help researchers understand the self-attention mechanism
  in transformers that allows these models to learn rich, contextual
  relationships between elements of a sequence. The main idea behind our method
  is to visualize a joint embedding of the query and key vectors used by
  transformer models to compute attention. Unlike previous attention
  visualization techniques, our approach enables the analysis of global patterns
  across multiple input sequences. We create an interactive visualization tool,
  AttentionViz, based on these joint query-key embeddings, and use it to study
  attention mechanisms in both language and vision transformers. We demonstrate
  the utility of our approach in improving model understanding and offering new
  insights about query-key interactions through several application scenarios and
  expert feedback.
paper_ai_summary: AttentionViz provides an interactive visualization technique for analyzing global attention patterns in transformer models, enhancing model understanding and providing insights into query-key interactions.
paper_ai_keywords: transformer models self-attention mechanism joint embedding query vectors key vectors interactive visualization tool AttentionViz