6 citations · 6 across the 5 of their papers we have counts for
5 papers
LLMStructBench: Benchmarking Large Language Model Structured Data Extraction
Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner
We present LLMStructBench, a novel benchmark for evaluating Large Language Models (LLMs) on extracting structured data and generating valid JavaScript Object Notation (JSON) output…
On the Domain Robustness of Contrastive Vision-Language Models
Mario Koddenbrock, Rudolf Hoffmann, David Brodmann +1
In real-world vision-language applications, practitioners increasingly rely on large, pretrained foundation models rather than custom-built solutions, despite limited transparency…
Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
Justus Westerhoff, Golzar Atefi, Mario Koddenbrock +4
The capacity of foundation models allows for their application to new, unseen tasks. The adaptation to such tasks is called transfer learning. An efficient transfer learning method…
Feedback-driven object detection and iterative model improvement
Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner
Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we pr…
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger +5
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their co…