18 citations · 53 across the 10 of their papers we have counts for
13 papers
Cross-Lingual Transferability of Training Data Extraction Attacks to Recover Memorized PII
Alexandru Nazare, Agnese Profico, Nicolò Vania +6
The robustness of Personally Identifiable Information (PII) protection in Large Language Models (LLMs) is a critical concern, yet the risks associated with cross-lingual data extra…
MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations
Elena Sofia Ruzzetti, Cornelius Emde, Sangdoo Yun +2
LLM agents are increasingly deployed in multi-party environments, handling sensitive personal data on behalf of individual users, for instance in group chats. When such an agent di…
Challenging the Abilities of Large Language Models in Italian: a Community Initiative
Malvina Nissim, Danilo Croce, Viviana Patti +78
The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of t…
Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models
Elena Sofia Ruzzetti, Giancarlo A. Xompero, Davide Venditti +1
Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, co…
MeMo: Towards Language Models with Associative Memory Mechanisms
Fabio Massimo Zanzotto, Elena Sofia Ruzzetti, Giancarlo A. Xompero +6
Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. In this paper, we propose a paradigm shift by designing an architecture…
Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli +3
Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive interne…
Enhancing Data Privacy in Large Language Models through Private Association Editing
Davide Venditti, Elena Sofia Ruzzetti, Giancarlo A. Xompero +4
Large language models (LLMs) require a significant redesign in solutions to preserve privacy in data-intensive applications due to their text-generation capabilities. Indeed, LLMs…
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation
Federico Ranaldi, Elena Sofia Ruzzetti, Dario Onorati +5
Understanding textual description to generate code seems to be an achieved capability of instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there is…
PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models
Leonardo Ranaldi, Elena Sofia Ruzzetti, Fabio Massimo Zanzotto
Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution…
A Trip Towards Fairness: Bias and De-Biasing in Large Language Models
Leonardo Ranaldi, Elena Sofia Ruzzetti, Davide Venditti +2
Cheap-to-Build Very Large-Language Models (CtB-LLMs) with affordable training are emerging as the next big revolution in natural language processing and understanding. These CtB-LL…
Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages
Elena Sofia Ruzzetti, Federico Ranaldi, Felicia Logozzo +3
The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language. In this paper, we propose a…
The Dark Side of the Language: Pre-trained Transformers in the DarkNet
Leonardo Ranaldi, Aria Nourbakhsh, Arianna Patrizi +4
Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In…
Lacking the embedding of a word? Look it up into a traditional dictionary
Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei +2
Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often…