18 citations · 74 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
Dissecting Clinical Reasoning in Language Models: A Comparative Study of Prompts and Model Adaptation Strategies
Mael Jullien, Marco Valentino, Leonardo Ranaldi +1
Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectivene…
Protoknowledge Shapes Behaviour of LLMs in Downstream Tasks: Memorization and Generalization with Knowledge Graphs
Federico Ranaldi, Andrea Zugarini, Leonardo Ranaldi +1
We introduce the concept of protoknowledge to formalize and measure how sequences of tokens encoding Knowledge Graphs are internalized during pretraining and utilized at inference…
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,…
Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
Leonardo Ranaldi, Barry Haddow, Alexandra Birch
Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through…
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…
Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions
Leonardo Ranaldi, Marco Valentino, Andrè Freitas
Chain-of-Though (CoT) represents a common strategy for reasoning in Large Language Models (LLMs) by decomposing complex tasks into intermediate inference steps. However, explanatio…