3 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
Large Language Models for Biomedical Text Simplification: Promising But Not There Yet
Zihao Li, Samuel Belkadi, Nicolo Micheletti +3
In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. Th…
Secure Control Systems for Autonomous Quadrotors against Cyber-Attacks
Samuel Belkadi
The problem of safety for robotic systems has been extensively studied. However, little attention has been given to security issues for three-dimensional systems, such as quadrotor…
Synthetic4Health: Generating Annotated Synthetic Clinical Letters
Libo Ren, Samuel Belkadi, Lifeng Han +2
Since clinical letters contain sensitive information, clinical-related datasets can not be widely applied in model training, medical research, and teaching. This work aims to gener…
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling
Samuel Belkadi, Libo Ren, Nicolo Micheletti +2
The vast amount of available medical records has the potential to improve healthcare and biomedical research. However, privacy restrictions make these data accessible for internal…
Exploration of Masked and Causal Language Modelling for Text Generation
Nicolo Micheletti, Samuel Belkadi, Lifeng Han +1
Large Language Models (LLMs) have revolutionised the field of Natural Language Processing (NLP) and have achieved state-of-the-art performance in practically every task in this fie…