7 citations · 8 across the 13 of their papers we have counts for
7 papers · 1 filter
Causally Fair Generation with Large Language Models
Patrik Okanovic, Torsten Hoefler, Drago Plecko
Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising conce…
Which LLM to pick? Online Active Model Selection for Large Language Models
Alessandro Turrin, Patrik Okanovic, Torsten Hoefler +1
Large Language Models (LLMs) are increasingly applied to process streaming data, with practitioners relying on benchmarks to select the best model even though these signals only ap…
Performance Foundations of Parallel & Distributed Reasoning Language Models
Maciej Besta, Leonard Schmidt, Lara Nonino +7
Reinforcement Learning with Verifiable Rewards (RLVR) and other RL-style post-training paradigms have been used for aligning large language models (LLMs) with reasoning standards.…
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
Boqian Wu, Qiao Xiao, Patrik Okanovic +6
Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not. In this work, we study sparse training in data-constrained r…
Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling
Qiao Xiao, Boqian Wu, Patrik Okanovic +6
Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we find that in large language model…
Confounder Detection via Treatment Intent: A New Observational Study Design
Drago Plecko, Patrik Okanovic, Torsten Hoefler +1
Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many ap…
Large Language Model Selection with Limited Annotations
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2
Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. T…