Papers by Giancarlo A. Xompero
Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) memorize and therefore, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII). |
| Approach: | They propose a method that uses a model knowledge to memorize PII from training data to mitigate the memorization of PI I. |
| Outcome: | The proposed method reduces the number of leaked PIIs in a number of configurations while making it more robust against privacy Training Data Extraction attacks. |
Position Paper: MeMo: Towards Language Models with Associative Memory Mechanisms (2025.findings-acl)
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Fabio Massimo Zanzotto, Elena Sofia Ruzzetti, Giancarlo A. Xompero, Leonardo Ranaldi, Davide Venditti, Federico Ranaldi, Cristina Giannone, Andrea Favalli, Raniero Romagnoli
| Challenge: | Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. |
| Approach: | They propose an architecture that explicitly memorizes sequences of tokens in layered associative memories. |
| Outcome: | The proposed architecture shows that memorization is a fundamental ability of large language models, achieved through learning. |