80 citations · 182 across the 20 of their papers we have counts for
22 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…
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:…
ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias
Rik Adriaensen, Lucas Van Praet, Jessa Bekker +3
Operationalizing definitions of fairness is difficult in practice, as multiple definitions can be incompatible while each being arguably desirable. Instead, it may be easier to dir…
Building and Measuring Trust between Large Language Models
Maarten Buyl, Yousra Fettach, Guillaume Bied +1
As large language models (LLMs) increasingly interact with each other, most notably in multi-agent setups, we may expect (and hope) that `trust' relationships develop between them,…
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices
Sander Noels, Guillaume Bied, Maarten Buyl +4
Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to…
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar +3
In recent years, the rapid development of artificial intelligence (AI) systems has raised concerns about our ability to ensure their fairness, that is, how to avoid discrimination…
AI Alignment at Your Discretion
Maarten Buyl, Hadi Khalaf, Claudio Mayrink Verdun +3
In AI alignment, extensive latitude must be granted to annotators, either human or algorithmic, to judge which model outputs are `better' or `safer.' We refer to this latitude as a…
Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications
Sander Noels, Alexander Rogiers, Maarten Buyl +1
The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, personalized, and interactive conten…
ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods
MaryBeth Defrance, Maarten Buyl, Tijl De Bie
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method…
Large Language Models Reflect the Ideology of their Creators
Maarten Buyl, Alexander Rogiers, Sander Noels +8
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These…
Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
Raphaël Romero, Maarten Buyl, Tijl De Bie +1
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that…
KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces
Alexander Rogiers, Maarten Buyl, Bo Kang +1
KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes ke…
fairret: a Framework for Differentiable Fairness Regularization Terms
Maarten Buyl, MaryBeth Defrance, Tijl De Bie
Current fairness toolkits in machine learning only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite…
RankFormer: Listwise Learning-to-Rank Using Listwide Labels
Maarten Buyl, Paul Missault, Pierre-Antoine Sondag
Web applications where users are presented with a limited selection of items have long employed ranking models to put the most relevant results first. Any feedback received from us…
Inherent Limitations of AI Fairness
Maarten Buyl, Tijl De Bie
As the real-world impact of Artificial Intelligence (AI) systems has been steadily growing, so too have these systems come under increasing scrutiny. In response, the study of AI f…
Tackling Algorithmic Disability Discrimination in the Hiring Process: An Ethical, Legal and Technical Analysis
Maarten Buyl, Christina Cociancig, Cristina Frattone +1
Tackling algorithmic discrimination against persons with disabilities (PWDs) demands a distinctive approach that is fundamentally different to that applied to other protected chara…
Optimal Transport of Classifiers to Fairness
Maarten Buyl, Tijl De Bie
In past work on fairness in machine learning, the focus has been on forcing the prediction of classifiers to have similar statistical properties for people of different demographic…
The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer
Maarten Buyl, Tijl De Bie
Learning and reasoning over graphs is increasingly done by means of probabilistic models, e.g. exponential random graph models, graph embedding models, and graph neural networks. W…
DeBayes: a Bayesian Method for Debiasing Network Embeddings
Maarten Buyl, Tijl De Bie
As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individ…