Localizing Paragraph Memorization in Language Models
Can we localize the weights and mechanisms used by a language model to
memorize and recite entire paragraphs of its training data? In this paper, we
show that while memorization is spread across multiple layers and model
components, gradients of memorized paragraphs have a distinguishable spatial
pattern, being larger in lower model layers than gradients of non-memorized
examples. Moreover, the memorized examples can be unlearned by fine-tuning only
the high-gradient weights. We localize a low-layer attention head that appears
to be especially involved in paragraph memorization. This head is predominantly
focusing its attention on distinctive, rare tokens that are least frequent in a
corpus-level unigram distribution. Next, we study how localized memorization is
across the tokens in the prefix by perturbing tokens and measuring the caused
change in the decoding. A few distinctive tokens early in a prefix can often
corrupt the entire continuation. Overall, memorized continuations are not only
harder to unlearn, but also to corrupt than non-memorized ones.
660a43fff4ab651901c081f3
Niklas Stoehr
660a43fff4ab651901c081f4
Mitchell Gordon
660a43fff4ab651901c081f5
Chiyuan Zhang
660a43fff4ab651901c081f6
Owen Lewis
Localizing Paragraph Memorization in Language Models
Can we localize the weights and mechanisms used by a language model to
memorize and recite entire paragraphs of its training data? In this paper, we
show that while memorization is spread across multiple layers and model
components, gradients of memorized paragraphs have a distinguishable spatial
pattern, being larger in lower model layers than gradients of non-memorized
examples. Moreover, the memorized examples can be unlearned by fine-tuning only
the high-gradient weights. We localize a low-layer attention head that appears
to be especially involved in paragraph memorization. This head is predominantly
focusing its attention on distinctive, rare tokens that are least frequent in a
corpus-level unigram distribution. Next, we study how localized memorization is
across the tokens in the prefix by perturbing tokens and measuring the caused
change in the decoding. A few distinctive tokens early in a prefix can often
corrupt the entire continuation. Overall, memorized continuations are not only
harder to unlearn, but also to corrupt than non-memorized ones.
Memorized paragraphs in language models exhibit distinguishable gradient patterns and can be localized to specific low-layer attention heads and distinctive tokens.
language model memorization gradients paragraph memorization attention head distinctive tokens unigram distribution decoding