1 citations · 1 across the 4 of their papers we have counts for
4 papers
How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling
Katrin Rohrbacher, Björn Nieth, Emmanuelle Salin +2
In this paper, we analyze how Large Language Models (LLMs) employ worldbuilding strategies, focusing on setting as one measurable dimension of storyworld construction. We compare 1…
How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
Björn Nieth, Marianna Gracheva, Michaela Mahlberg +2
While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, the fundamental question of how human-like generated texts…
Effective Data Pruning through Score Extrapolation
Sebastian Schmidt, Prasanga Dhungel, Christoffer Löffler +3
Training advanced machine learning models demands massive datasets, resulting in prohibitive computational costs. To address this challenge, data pruning techniques identify and re…
Large-Scale Dataset Pruning in Adversarial Training through Data Importance Extrapolation
Björn Nieth, Thomas Altstidl, Leo Schwinn +1
Their vulnerability to small, imperceptible attacks limits the adoption of deep learning models to real-world systems. Adversarial training has proven to be one of the most promisi…