1 citations · 1 across the 6 of their papers we have counts for
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Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
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…
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…
Generating Medical Prescriptions with Conditional Transformer
Samuel Belkadi, Nicolo Micheletti, Lifeng Han +2
Access to real-world medication prescriptions is essential for medical research and healthcare quality improvement. However, access to real medication prescriptions is often limite…