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Copy-suppression: a head anti-copies whatever token it would predict

measured in 1 paper

McDougall et al. compose GPT-2 Small head 10.7's OV circuit with the model's embedding and unembedding into an explicit vocab-by-vocab matrix [mcdougall-etal-2023-copy-suppression-comprehensively-understanding-an-attention-head] Its diagonal, each token's self-projection through the circuit, is among the top-10 most negative entries in 84.70% of vocabulary columns [mcdougall-etal-2023-copy-suppression-comprehensively-understanding-an-attention-head] The composed QK circuit's diagonal is the largest entry in its row for 95.72% of tokens, so the head attends to then anti-copies whichever token it would otherwise predict [mcdougall-etal-2023-copy-suppression-comprehensively-understanding-an-attention-head] A projection-only ablation (CSPA) onto the per-token negative-unembedding direction preserves 76.9-95.2% of the head's effect, confirming the geometric claim is causally load-bearing [mcdougall-etal-2023-copy-suppression-comprehensively-understanding-an-attention-head]

Context

each vocabulary token's own unembedding vector as a per-token direction, and a circuit's diagonal self-projection onto it as a quantified geometric property of the circuit's weights, projection-only ablation (CSPA) onto a per-token direction as a causal validation of a weight-space geometric claim

Confirmed in models

Papers

Copy Suppression: Comprehensively Understanding an Attention Head — McDougall, Callum, Conmy, Arthur, Rushing, Cody, McGrath, Thomas, Nanda, Neel2023 · arXiv:2310.04625