27 citations · 31 across the 8 of their papers we have counts for
14 papers
Reversing Arrows in Large Language Models
Sefika Efeoglu, Adrian Paschke
Large language models (LLMs) have achieved strong performance on text-to-knowledge graph generation and related tasks. Nevertheless, it is still unclear whether they accurately mod…
Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian
Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma +2
Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross-lingual RE for Romanian by co…
Benchmarking Quantum Architecture Search with Surrogate Assistance
Darya Martyniuk, Johannes Jung, Daniel Barta +1
The development of quantum algorithms and their practical applications currently relies heavily on the efficient design, compilation, and optimization of quantum circuits. In parti…
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
Daniel Barta, Darya Martyniuk, Johannes Jung +1
Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a genera…
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
Kumar Manas, Christian Schlauch, Adrian Paschke +2
Deep learning-based trajectory prediction models have demonstrated promising capabilities in capturing complex interactions. However, their out-of-distribution generalization remai…
Post-Training Language Models for Continual Relation Extraction
Sefika Efeoglu, Adrian Paschke, Sonja Schimmler
Real-world data, such as news articles, social media posts, and chatbot conversations, is inherently dynamic and non-stationary, presenting significant challenges for constructing…