7 citations · 12 across the 12 of their papers we have counts for
5 papers · 1 filter
Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
Lucio La Cava, Luca Maria Aiello, Andrea Tagarelli
Generative Artificial Intelligence is reshaping online communication by enabling large-scale production of Machine-Generated Text (MGT) at low cost. While its presence is rapidly g…
Toward Preference-aligned Large Language Models via Residual-based Model Steering
Lucio La Cava, Andrea Tagarelli
Preference alignment is a critical step in making Large Language Models (LLMs) useful and aligned with (human) preferences. Existing approaches such as Reinforcement Learning from…
Authorship Attribution in Multilingual Machine-Generated Texts
Lucio La Cava, Dominik Macko, Róbert Móro +2
As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult…
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
Lucio La Cava, Andrea Tagarelli
Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We propose OpenTuringBench, a new be…
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks
Lucio La Cava, Domenico Mandaglio, Lorenzo Zangari +1
Link prediction algorithms for multilayer networks are in principle required to effectively account for the entire layered structure while capturing the unique contexts offered by…