1 citations · 2 across the 4 of their papers we have counts for
4 papers
Cost-Optimal LLM Routing with Limited User Feedback under User Satisfaction Guarantees
Herbert Woisetschläger, Arastun Mammadli, Ryan Zhang +1
Inference costs for large language model (LLM) applications are rapidly growing, driven by surging demand and rising infrastructure cost. Users expect high-quality responses, and i…
Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
Shiqiang Wang, Herbert Woisetschläger, Hans Arno Jacobsen +1
Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, including training, tuning, a…
Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining
Daouda Sow, Herbert Woisetschläger, Saikiran Bulusu +3
Pretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current…
Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis
Herbert Woisetschläger, Simon Mertel, Christoph Krönke +2
The European Union Artificial Intelligence Act mandates clear stakeholder responsibilities in developing and deploying machine learning applications to avoid substantial fines, pri…