2 citations · 3 across the 6 of their papers we have counts for
9 papers
Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata
Pedro Bernardo, Alex Ploner, Marco Squarcina +2
Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…
Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection
Yassine Turki, Vinko Sabolčec, Bettina Messmer +1
As Large Language Models (LLMs) scale, data curation has shifted from maximizing volume to optimizing the signal-to-noise ratio by performing quality filtering. However, for many l…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
Guilherme Penedo, Hynek Kydlíček, Vinko Sabolčec +7
Pre-training state-of-the-art large language models (LLMs) requires vast amounts of clean and diverse text data. While the open development of large high-quality English pre-traini…
Enhancing Multilingual LLM Pretraining with Model-Based Data Selection
Bettina Messmer, Vinko Sabolčec, Martin Jaggi
Dataset curation has become a basis for strong large language model (LLM) performance. While various rule-based filtering heuristics exist for English and multilingual datasets, mo…
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
Atli Kosson, Bettina Messmer, Martin Jaggi
Learning Rate Warmup is a popular heuristic for training neural networks, especially at larger batch sizes, despite limited understanding of its benefits. Warmup decreases the upda…
On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists
Dongyang Fan, Bettina Messmer, Nikita Doikov +1
On-device LLMs have gained increasing attention for their ability to enhance privacy and provide a personalized user experience. To facilitate private learning with scarce data, Fe…
Towards an empirical understanding of MoE design choices
Dongyang Fan, Bettina Messmer, Martin Jaggi
In this study, we systematically evaluate the impact of common design choices in Mixture of Experts (MoEs) on validation performance, uncovering distinct influences at token and se…
Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks
Atli Kosson, Bettina Messmer, Martin Jaggi
This study investigates how weight decay affects the update behavior of individual neurons in deep neural networks through a combination of applied analysis and experimentation. We…