Memorisation is a natural part of learning from real-world data: neural models pick up on atypical input-output combinations and store those training examples in their parameter space. That this happens is well-known, but how and where are questions that remain largely unanswered. Given a multi-layered neural model, where does memorisation occur in the millions of parameters? Related work reports conflicting findings: a dominant hypothesis based on image classification is that lower layers learn generalisable features and that deeper layers specialise and memorise. Work from NLP suggests this does not apply to language models, but has been mainly focused on memorisation of facts. We expand the scope of the localisation question to 12 natural language classification tasks and apply 4 memorisation localisation techniques. Our results indicate that memorisation is a gradual process rather than a localised one, establish that memorisation is task-dependent, and give nuance to the generalisation first, memorisation second hypothesis.

本研究解决了在自然语言分类任务中记忆发生的位置这一问题，挑战了现有的理论观点。通过探索12个任务并运用4种记忆定位技术，发现记忆是一个渐进的过程而非局部化的，并且与具体任务相关，提供了对“优先概括，后续记忆”假设的新视角。

优先概括，后续记忆？自然语言分类任务中的记忆定位