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To visualize a transformer’s attention weights, run the model on a short input with attention output enabled, then plot the resulting weights against the tokenizer’s actual tokens. Use a head view to inspect one layer and head, or a model view to compare heads and layers. BertViz offers both interactive views when your model returns weights in a compatible format. Treat the display as a view of the model’s computation—not, by itself, an explanation of why the model made a prediction.
Choose a view that matches your question
| What you want to inspect | Useful view | What it shows and its limits |
|---|---|---|
| Which token positions one attention head attends to | BertViz head view or an attention matrix/heatmap | Shows token-to-token weights for a selected head. Record the layer, head, and tokenization; the map alone does not explain the model’s final prediction. BertViz project |
| How heads or layers differ | BertViz model view | Provides a broader comparison across layers and heads. Long inputs or large models can make interactive rendering slow. BertViz project |
| How attention patterns combine across layers | Attention rollout | Aggregates attention maps across layers. It is still an attention-based summary, not definitive causal attribution. Chefer, Gur, and Wolf (2021) |
| Global structure in query/key representations | AttentionViz | A research visualization approach based on joint query/key embeddings, described for language and vision transformers. AttentionViz |
| Neurons in query/key vectors | BertViz neuron view | Its documented support is narrower than the head and model views: custom BERT, GPT-2, and RoBERTa implementations. BertViz project |
Prepare attention weights for visualization
- Choose a short, interpretable input. A concise sentence or passage makes token relationships easier to inspect. Large models and long inputs may slow BertViz; displaying fewer layers can help.
- Run the model with attention output enabled. The exact setting and output structure depend on the model library and architecture. Confirm that the model actually returned attention tensors before passing them to a plotting tool.
- Check the tool’s expected format. BertViz’s head and model views require attention weights arranged in a compatible format. Model and software-stack differences can affect whether weights are available and how they are represented. Consult the BertViz documentation for its supported input conventions.
- Match the displayed tokens to the tokenizer output. Preserve the tokenizer’s actual token boundaries, including subword pieces and special tokens. Do not silently replace them with whole words: the plotted positions correspond to the model’s token sequence.
- Label the computation you are showing. Include the model and tokenizer, exact input, layer, head, and whether the map represents self-attention or encoder-decoder attention. A tensor’s arrangement and the view supplied determine what the display represents.
Read a head view and a model view
Head view: inspect token-to-token weights
A head-level view focuses on the weights assigned by one attention head in one layer. Use it to inspect which input positions receive more or less weight for the selected computation. Keep the layer and head visible in notes or figure captions; patterns can differ across both.
Model view: compare layers and heads
A model view helps scan multiple heads and layers for patterns or contrasts. It is useful for exploration, but a broad display does not remove the need to identify the input and selected model. For large models or long sequences, reduce the number of displayed layers if rendering is slow.
Rollout: summarize across layers cautiously
Attention rollout combines attention maps across layers to produce a cross-layer summary. Chefer, Gur, and Wolf discuss it as a baseline in broader work on transformer interpretability. Compare a rollout with individual maps; the aggregation remains attention-based and should not be presented as proof that a token caused a prediction. Read the paper.
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What an attention visualization can—and cannot—tell you
An attention map describes a particular model run and one part of its computation. It can help inspect patterns, compare heads, and explore how attention is distributed over token positions. It does not automatically establish why a model produced an output.
The BertViz project cautions that “Visualizing attention weights illuminates one type of architecture within the model but does not necessarily provide a direct explanation for predictions.” BertViz documentation.
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Jain and Wallace’s paper, Attention is not Explanation, reports that learned attention weights can diverge from gradient-based feature-importance measures and that substantially different attention distributions can yield equivalent predictions. This argues against treating attention as a universal stand-alone explanation method; it does not make attention maps useless for examining the computation. Read the paper.
If your question is causal—whether a token drove a prediction—an attention picture alone is insufficient. Describe the plotted weights as attention patterns, and use a separate attribution or intervention analysis when making claims about explanatory importance.
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Other visualization research
Jesse Vig’s 2019 work presents multiscale visualizations demonstrated on BERT and GPT-2, with use cases including bias detection, attention-head localization, and linking neuron behavior. These are research-oriented visualization ideas rather than a guarantee that every transformer exposes the same data or interface. Read the paper.
AttentionViz takes a different approach, using joint query/key embeddings to explore global attention structure in language and vision transformers. As with other methods, the display is a lens on model representations, not by itself a causal explanation. Read the paper.
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