6 papers
Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines
Maximilian Böther, Josh Wills, Ties Robroek +14
Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costly ablations are caused by the…
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen +33
Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…
20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone
DatologyAI, :, Siddharth Joshi +32
Data curation has shifted the quality-compute frontier for language-model and contrastive image-text pretraining, but its role for vision-language models (VLMs) is far less establi…
The Finetuner's Fallacy: When to Pretrain with Your Finetuning Data
Christina Baek, Ricardo Pio Monti, David Schwab +31
Real-world model deployments demand strong performance on narrow domains where data is often scarce. Typically, practitioners finetune models to specialize them, but this risks ove…
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…
Efficiently Computing Directed Minimum Spanning Trees
Maximilian Böther, Otto Kißig, Christopher Weyand
Computing a directed minimum spanning tree, called arborescence, is a fundamental algorithmic problem, although not as common as its undirected counterpart. In 1967, Edmonds discus…