18 citations · 74 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
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…
HANS, are you clever? Clever Hans Effect Analysis of Neural Systems
Leonardo Ranaldi, Fabio Massimo Zanzotto
Instruction-tuned Large Language Models (It-LLMs) have been exhibiting outstanding abilities to reason around cognitive states, intentions, and reactions of all people involved, le…
Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations
Leonardo Ranaldi, Giulia Pucci, Andre Freitas
The language ability of Large Language Models (LLMs) is often unbalanced towards English because of the imbalance in the distribution of the pre-training data. This disparity is de…
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…