164 citations · 411 across the 16 of their papers we have counts for
17 papers
Gender Bias in MT for a Genderless Language: New Benchmarks for Basque
Amaia Murillo, Olatz-Perez-de-Viñaspre, Naiara Perez
Large language models (LLMs) and machine translation (MT) systems are increasingly used in our daily lives, but their outputs can reproduce gender bias present in the training data…
Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments
Romain Froger, Pierre Andrews, Matteo Bettini +21
We introduce Gaia2, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
ARE: Scaling Up Agent Environments and Evaluations
Romain Froger, Pierre Andrews, Matteo Bettini +21
We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of age…
SEA-LION: Southeast Asian Languages in One Network
Raymond Ng, Thanh Ngan Nguyen, Yuli Huang +28
Recently, Large Language Models (LLMs) have dominated much of the artificial intelligence scene with their ability to process and generate natural languages. However, the majority…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models
Youssef Benchekroun, Megi Dervishi, Mark Ibrahim +7
We propose WorldSense, a benchmark designed to assess the extent to which LLMs are consistently able to sustain tacit world models, by testing how they draw simple inferences from…
GAIA: a benchmark for General AI Assistants
Grégoire Mialon, Clémentine Fourrier, Craig Swift +3
We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of funda…
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Grégoire Mialon, Quentin Garrido, Hannah Lawrence +3
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering…
A Cookbook of Self-Supervised Learning
Randall Balestriero, Mark Ibrahim, Vlad Sobal +16
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art wi…
On Inductive Biases for Machine Learning in Data Constrained Settings
Grégoire Mialon
Learning with limited data is one of the biggest problems of machine learning. Current approaches to this issue consist in learning general representations from huge amounts of dat…
Augmented Language Models: a Survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli +10
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially comple…
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations
Grégoire Mialon, Randall Balestriero, Yann LeCun
Self-Supervised Learning (SSL) methods such as VICReg, Barlow Twins or W-MSE avoid collapse of their joint embedding architectures by constraining or regularizing the covariance ma…
GraphiT: Encoding Graph Structure in Transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse +1
We show that viewing graphs as sets of node features and incorporating structural and positional information into a transformer architecture is able to outperform representations l…
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont +1
We address the problem of learning on sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range depend…
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions
Grégoire Mialon, Alexandre d'Aspremont, Julien Mairal
We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We derive loss functions that produce…
A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen +1
We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it ad…