3 citations · 8 across the 12 of their papers we have counts for
12 papers
Apollo Restore: A Foundation LLM for Historical Greek Optimized for Fill-in-the-Middle Restoration of Ancient Greek Texts
Hope McGovern, Anna Dolganov, Samuel Belkadi +3
We present Apollo Restore, a 24-billion-parameter large language model for restoring lacunae---physical gaps---in fragmentary Ancient Greek texts. Fine-tuned from Mistral Small wit…
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…
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
Multi-scale Autoregressive Models are Laplacian, Discrete, and Latent Diffusion Models in Disguise
Steve Hong, Samuel Belkadi
We reinterpret Visual Autoregressive (VAR) models as iterative refinement models to identify which design choices drive their quality-efficiency trade-off. Instead of treating VAR…
Diffusion-Free Graph Generation with Next-Scale Prediction
Samuel Belkadi, Steve Hong, Marian Chen +3
Autoregressive models excel in efficiency and plug directly into the transformer ecosystem, delivering robust generalization, predictable scalability, and seamless workflows such a…
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…
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…
Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts
Zihao Li, Samuel Belkadi, Nicolo Micheletti +3
Biomedical literature often uses complex language and inaccessible professional terminologies. That is why simplification plays an important role in improving public health literac…