Papers by Elena Sofia Ruzzetti
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. |
Lexical Popularity: Quantifying the Impact of Pre-training for LLM Performance (2026.eacl-long)
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| Challenge: | Large Language Models excel in varied tasks, but their mechanisms remain unclear . current LLMs' development has put this assumption in jeopardy, authors say . |
| Approach: | They examine whether LLMs learn generalized linguistic abstraction or rely on surface-level features that match their pre-training data. |
| Outcome: | The proposed model can learn generalized linguistic abstraction or rely on surface-level features that match their pre-training data. |
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL translation (2024.findings-acl)
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Federico Ranaldi, Elena Sofia Ruzzetti, Dario Onorati, Leonardo Ranaldi, Cristina Giannone, Andrea Favalli, Raniero Romagnoli, Fabio Massimo Zanzotto
| Challenge: | Large Language Models (LLMs) understand textual description to generate code in zero-shot scenarios, but there is a possibility that this ability may be influenced by having seen target textual descriptions and the related code. |
| Approach: | They propose a method to detect Data Contamination in Large Language Models (LLMs) and analyze their results on Termite and Spider Datasets to investigate their method. |
| Outcome: | The proposed method detects data contamination in GPTs and analyzes its performance on unfamiliar datasets. |
Lacking the Embedding of a Word? Look it up into a Traditional Dictionary (2022.findings-acl)
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Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei, Francesca Fallucchi, Noemi Scarpato, Fabio Massimo Zanzotto
| Challenge: | Word embeddings are powerful dictionaries, but they fail to give sense to rare words . a large body of research is devoted to devising ways to capture word meaning . |
| Approach: | They propose to use definitions retrieved from traditional dictionaries to build word embeddings for rare words. |
| Outcome: | The proposed methods outperform state-of-the-art methods for embeddings of unknown words . the proposed methods significantly outperformed the BERT method for OOV words compared to the proposed method . |
A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages (2024.findings-naacl)
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| Challenge: | Existing methods for eliciting Large Language Models (LLMs) to solve complex tasks are limited to English due to the imbalance in the distribution of pre-training data. |
| Approach: | They propose a method for aligning Cross-lingual CoT reasoning across languages . they propose eliciting Large Language Models to solve complex tasks step-by-step . |
| Outcome: | The proposed method outperforms existing prompting methods by reducing interactions and achieving state-of-the-art performance. |
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. |