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20242026
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cs.CL2026

Abstract Activation Spaces for Content-Invariant Reasoning in Large Language Models

Gabriele Maraia, Marco Valentino, Fabio Massimo Zanzotto +1

Large Language Models (LLMs) often struggle with deductive judgment in syllogistic reasoning, systematically conflating semantic plausibility with formal validity a phenomenon know…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour

Leonardo Ranaldi, Giulia Pucci

Large Language Models have been demonstrating broadly satisfactory generative abilities for users, which seems to be due to the intensive use of human feedback that refines respons…