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.
Related event: Pitfalls in Analyzing GPT-2 Attention Maps(2 posts)→
More from Research
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11
- Fruit fly brain as an LLM: connectome-driven language model demo goes live — ngxson · 2026-09-11
- Harry Collins: LLMs can't do frontier science because they can't invent new language — whoamisri · 2026-09-11
- The Waymo effect: how AI is quietly making research less collaborative — JohnHammersley · 2026-09-11
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11