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arXiv Machine Learning By Aryan Chowdhury, Riza Batista-Navarro

Fewer Heads, Fewer Memories: Pruning Redundant Attention Heads Suppresses Verbatim Memorisation

Read the original on arXiv Machine Learning →

Large language models can memorize and reproduce verbatim training data, raising privacy and IP concerns. Recent theory links attention heads to this memorisation, while empirical work shows many heads are redundant and can be pruned with little performance loss. This study shows that zeroing out a large fraction of redundant heads before inference dramatically reduces memorisation (91.5% drop) and even improves language‑modeling performance on GPT‑2‑XL.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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