57 citations · 58 across the 7 of their papers we have counts for
7 papers
Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications
Jakob Sturm, Josef Pichlmeier, Christian Bernhard +4
Large Language Models (LLMs) are increasingly employed in enterprise question-answering (QA) systems, requiring adaptation to domain-specific knowledge. Among the most prevalent me…
XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity
Dasol Choi, Eugenia Kim, Jaewon Noh +14
Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely evaluate a model's a…
Harnessing Chain-of-Thought Metadata for Task Routing and Adversarial Prompt Detection
Ryan Marinelli, Josef Pichlmeier, Tamas Bisztray
In this work, we propose a metric called Number of Thoughts (NofT) to determine the difficulty of tasks pre-prompting and support Large Language Models (LLMs) in production context…
Performance Characterization of Expert Router for Scalable LLM Inference
Josef Pichlmeier, Philipp Ross, Andre Luckow
Large Language Models (LLMs) have experienced widespread adoption across scientific and industrial domains due to their versatility and utility for diverse tasks. Nevertheless, dep…
Quantum Computing: Towards Industry Reference Problems
Andre Luckow, Johannes Klepsch, Josef Pichlmeier
The complexity is increasing rapidly in many areas of the automotive industry. The design of an automobile involves many different engineering disciplines, e. g., mechanical, elect…
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
Thomas Hubregtsen, Josef Pichlmeier, Patrick Stecher +1
An active area of investigation in the search for quantum advantage is Quantum Machine Learning. Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-cl…
Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier +3
Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attentio…