Pitfalls in Analyzing GPT-2 Attention Maps
its_vayishu · x · 2026-07-16
The author documents three instances of being misled by attention maps while analyzing the topological structure of GPT-2:
- Threshold artifacts: Initial structural features observed were merely artifacts caused by threshold settings.
- Attention sink interference: Trained attention heads initially appeared highly stable across different inputs (p=2e-26), but this stability vanished entirely (p=0.32) after excluding sink tokens like BOS (Beginning of Sequence).
The author concludes that Top-K attention maps can easily create illusions during analysis, and this testing process can serve as a checklist for troubleshooting such analytical fallacies.
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