369 citations · 528 across the 19 of their papers we have counts for
21 papers
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…
ECKO: Explainable Clinical Knowledge for Oncology
Marta Contreiras Silva, Daniel Faria, Laura Balbi +13
Personalized oncology aims to tailor treatment strategies to the unique molecular and clinical profiles of individual patients, moving beyond the traditional paradigm of treating 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…
Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations
Leonardo Ranaldi, Federico Ranaldi, Fabio Massimo Zanzotto +2
Retrieval-augmented generation (RAG) is key to enhancing large language models (LLMs) to systematically access richer factual knowledge. Yet, using RAG brings intrinsic challenges,…
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…
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…
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models
Michele Mastromattei, Fabio Massimo Zanzotto
Neural network pruning has become increasingly crucial due to the complexity of these models and their widespread use in various fields. Existing pruning algorithms often suffer fr…
Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
Leonardo Ranaldi, Giulia Pucci, Federico Ranaldi +2
Reasoning methods, best exemplified by the well-known Chain-of-Thought (CoT), empower the reasoning abilities of Large Language Models (LLMs) by eliciting them to solve complex tas…
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…
Active Informed Consent to Boost the Application of Machine Learning in Medicine
Marco Gerardi, Katarzyna Barud, Marie-Catherine Wagner +5
Machine Learning may push research in precision medicine to unprecedented heights. To succeed, machine learning needs a large amount of data, often including personal data. Therefo…
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…
Every time I fire a conversational designer, the performance of the dialog system goes down
Giancarlo A. Xompero, Michele Mastromattei, Samir Salman +4
Incorporating explicit domain knowledge into neural-based task-oriented dialogue systems is an effective way to reduce the need of large sets of annotated dialogues. In this paper,…
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…
When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey
Antonio-Jesús Banegas-Luna, Jorge Peña-García, Adrian Iftene +6
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. In a few decades, computers may be capable of formulating diagnoses…
GASP! Generating Abstracts of Scientific Papers from Abstracts of Cited Papers
Fabio Massimo Zanzotto, Viviana Bono, Paola Vocca +4
Creativity is one of the driving forces of human kind as it allows to break current understanding to envision new ideas, which may revolutionize entire fields of knowledge. Scienti…
Human-in-the-loop Artificial Intelligence
Fabio Massimo Zanzotto
Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future h…
Parsing with CYK over Distributed Representations
Fabio Massimo Zanzotto, Giordano Cristini, Giorgio Satta
Syntactic parsing is a key task in natural language processing. This task has been dominated by symbolic, grammar-based parsers. Neural networks, with their distributed representat…
Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
Lorenzo Ferrone, Fabio Massimo Zanzotto
Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to cont…
Distributed Tree Kernels
Fabio Massimo Zanzotto, Lorenzo Dell'Arciprete
In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and space complexity of tree kernels. Using a linear complexity algorithm to compute v…