System 2 Attention: don't answer the messy context — regenerate a clean one first, then answer that
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A transformer's attention is soft : every token in the context gets some weight, and nothing is ever weighted to exactly zero. So a confident wrong hint dropped into a prompt ("I'm pretty sure it was 2008") or a stray off-topic sentence still tugs the answer — the model agrees with the opinion ( sycophancy ) or leans on an irrelevant detail ( spurious correlation ). System 2 Attention (S2A, Weston & Sukhbaatar 2023) fixes this without asking the model to try harder. I built an interactive…
1Key Takeaways
- A transformer's attention is soft : every token in the context gets some weight, and nothing is ever weighted to exactly zero.
- So a confident wrong hint dropped into a prompt ("I'm pretty sure it was 2008") or a stray off-topic sentence still tugs the answer — the model agrees with the opinion ( sycophancy ) or leans on an irrelevant detail ( spurious correlation ).
- System 2 Attention (S2A, Weston & Sukhbaatar 2023) fixes this without asking the model to try harder.
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that a transformer's attention is soft : every token in the context gets some weight, and nothing is ever weighted to exactly zero.
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