Workflow · workflow
Visualizing transformer attention with BertViz and Comet for model explainability
Transformer models are notoriously opaque, making it difficult to explain how or why they reach their outputs, which risks undetected bias, model collapse, and ethical issues — especially as models are deployed in sensitive domains like healthcare, law, finance, and security.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Run LLM on input prompts
Pre-trained language models are run on input prompts for tasks such as text generation, question-answering, and sentiment analysis.
Tools used
BertVizCometGPT-2HuggingFace
Outcome
BertViz provides multi-scale attention visualization that is simple and intuitive; logging plots to Comet enables practitioners to compare model attention across runs and apply the views to detect potential bias.
What failed first
Earlier attention visualization approaches were overly complicated, failed to translate to non-technical audiences, and varied greatly across projects and use cases.
Grounding & classification
Source type: technical build writeup
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