activity
20232026
most citedWhen Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour

10 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generation

Giulia Pucci, Ruizhe Li, Arabella Sinclair

This paper investigates structural priming in language model (LM) production, examining how preceding structural context influences sentence completion. While prior work has demons…

cs.AI2026

Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback

Giulia Pucci, Emily Hemendinger, Ruizhe Li +3

Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems. A…

cs.CL2024

Animate, or Inanimate, That is the Question for Large Language Models

Leonardo Ranaldi, Giulia Pucci, Fabio Massimo Zanzotto

The cognitive essence of humans is deeply intertwined with the concept of animacy, which plays an essential role in shaping their memory, vision, and multi-layered language underst…

cs.CL2023★ 10 cited

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…

cs.CL2023★ 3 cited

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…

math.OC2023★ 2 cited

Impulse control of conditional McKean-Vlasov jump diffusions

Nacira Agram, Giulia Pucci, Bernt Oksendal

This paper establishes a verification theorem for impulse control problems involving conditional McKean-Vlasov jump diffusions. We obtain a Markovian system by combining the state…