Papers by Andrea Esuli
Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors (2025.findings-acl)
Copied to clipboard
Andrea Pedrotti, Michele Papucci, Cristiano Ciaccio, Alessio Miaschi, Giovanni Puccetti, Felice Dell’Orletta, Andrea Esuli
| Challenge: | Recent advances in Generative AI and Large Language Models (LLMs) have enabled the creation of highly realistic synthetic content, raising concerns about the potential for malicious use, such as misinformation and manipulation. |
| Approach: | They evaluate the resilience of state-of-the-art MGT detectors to linguistically informed adversarial attacks by using Direct Preference Optimization to shift the MGT style toward human-written text. |
| Outcome: | The proposed pipeline fine-tunes language models to shift the MGT style toward human-written text (HWT) it obtains generations more challenging to detect by current models, and shows that detectors can be easily fooled with relatively few examples, resulting in a significant drop in detecting performances. |
The Invalsi Benchmarks: measuring the Linguistic and Mathematical understanding of Large Language Models in Italian (2025.coling-main)
Copied to clipboard
| Challenge: | Invalsi MATE is a high-resource language, but there are few benchmarks to evaluate generative Large Language Models in this language. |
| Approach: | They propose three benchmarks to evaluate language models on mathematical understanding in italian . they use the Invalsi tests, which are administered to students aged 6 to 18 in the italian school system . |
| Outcome: | The proposed benchmarks are based on the Invalsi tests and the Italian highschool math Olympics. |
Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation (2025.findings-naacl)
Copied to clipboard
Luca Moroni, Giovanni Puccetti, Pere-Lluís Huguet Cabot, Andrei Stefan Bejgu, Alessio Miaschi, Edoardo Barba, Felice Dell’Orletta, Andrea Esuli, Roberto Navigli
| Challenge: | Pretrained Large Language Models (LLMs) are mainly designed for the English language, but are not optimized for non-English languages due to language contamination or multilingual pretraining data. |
| Approach: | They propose a method that leverages neural mapping for vocabulary substitution to optimize LLMs for the Italian language. |
| Outcome: | The proposed method reduces token fertility by 25% and improves grounded alignment strategies. |