Section 117 · Chapter 15, How Models Work
Visualizing, Debugging, and Editing LLM Concepts
Modern interpretability tools can reveal useful clues inside models, but they are instruments, not magic explanations.
interpretabilityattentionactivationsparse autoencodervisualizing debugging editing llm concepts
What to do
- Treat internal-model evidence as one signal alongside behavioral evals, production traces, and expert review.
- Define runnable checks that exercise interpretability, attention, and activation.
- Set acceptable outcomes and blocker failures for interpretability, attention, and activation before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for interpretability, attention, activation, sparse autoencoder needed to reproduce work on Visualizing, Debugging, and Editing LLM Concepts.
- Report results for interpretability, attention, activation, sparse autoencoder by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
At scale, combine interpretability with causal tests: activation patching, counterfactual prompts, feature steering, and behavior evals before and after intervention. Model editing should always be regression-tested broadly because changing one concept can move unrelated behavior.
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Cite this page
Jason Arbon. "Visualizing, Debugging, and Editing LLM Concepts." Testing AI Knowledge Edition, section 117.
https://jarbon.ai/testing-ai/knowledge/ch117-visualizing-debugging-editing-llm-concepts.html