Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks (2024.findings-acl)
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| Challenge: | Memorisation in neural models is concerned due to overfitting and privacy concerns . a dominant hypothesis based on image classification is that lower layers learn generalisable features and deeper layers specialise and memorise. |
| Approach: | They apply 4 techniques to localise and edit models' memories. |
| Outcome: | The proposed method shows that memorisation is a gradual process rather than a localised one. |
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| Challenge: | Using the counterfactual memorisation metric, we find that when training neural networks, models will memorise some inputs but not others. |
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