ALiBi: give a Transformer position sense with one line, and let it test longer than it trained
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Self-attention is order-blind: shuffle the tokens and the raw QKᵀ scores come out identical, so a Transformer has to be told where each token sits. The classic fix adds a positional embedding to every token vector — sinusoidal or learned. But those absolute codes are welded to the exact positions seen in training. Feed a model trained at length 1024 a sequence of 3000 and it meets position indices it has never represented, and quality collapses. ALiBi (Press, Smith & Lewis, 2021 — "Train…
1Key Takeaways
- Self-attention is order-blind: shuffle the tokens and the raw QKᵀ scores come out identical, so a Transformer has to be told where each token sits.
- The classic fix adds a positional embedding to every token vector — sinusoidal or learned.
- But those absolute codes are welded to the exact positions seen in training.
- Feed a model trained at length 1024 a sequence of 3000 and it meets position indices it has never represented, and quality collapses.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that self-attention is order-blind: shuffle the tokens and the raw QKᵀ scores come out identical, so a Transformer has to be told where each token sits.
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