2 citations · 5 across the 12 of their papers we have counts for
13 papers
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…
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
Philippe Formont, Maxime Darrin, Ismail Ben Ayed +1
Recent reasoning-based large language models have shown strong performance on tasks with verifiable outcomes, but their use in de novo molecular generation remains limited by the l…
Voxtral Realtime
Mistral-AI, :, Alexander H. Liu +166
We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adap…
Ministral 3
Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116
We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…
Learning Task-Agnostic Representations through Multi-Teacher Distillation
Philippe Formont, Maxime Darrin, Banafsheh Karimian +5
Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, i…
Devstral: Fine-tuning Language Models for Coding Agent Applications
Abhinav Rastogi, Adam Yang, Albert Q. Jiang +100
We introduce Devstral-Small, a lightweight open source model for code agents with the best performance among models below 100B size. In this technical report, we give an overview o…
Statistical Deficiency for Task Inclusion Estimation
Loïc Fosse, Frédéric Béchet, Benoît Favre +5
Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any t…
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
Maxime Darrin, Ines Arous, Pablo Piantanida +1
Scientific peer review is essential for the quality of academic publications. However, the increasing number of paper submissions to conferences has strained the reviewing process.…
When is an Embedding Model More Promising than Another?
Maxime Darrin, Philippe Formont, Ismail Ben Ayed +2
Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks. The eva…
: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime Darrin, Philippe Formont, Jackie Chi Kit Cheung +1
Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capaci…
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Câmara Gomes +3
Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number of AI-based systems. Existing OOD textu…
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data
Maxime Darrin, Pablo Piantanida, Pierre Colombo
Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots, is essential. A key ingredient to ensu…