5 papers
Ensemble Watermarks for Large Language Models
Georg Niess, Roman Kern
As large language models (LLMs) reach human-like fluency, reliably distinguishing AI-generated text from human authorship becomes increasingly difficult. While watermarks already e…
Four Guiding Principles for Modeling Causal Domain Knowledge: A Case Study on Brainstorming Approaches for Urban Blight Analysis
Houssam Razouk, Michael Leitner, Roman Kern
Urban blight is a problem of high interest for planning and policy making. Researchers frequently propose theories about the relationships between urban blight indicators, focusing…
Evaluating Large Language Models for Causal Modeling
Houssam Razouk, Leonie Benischke, Georg Niess +1
In this paper, we consider the process of transforming causal domain knowledge into a representation that aligns more closely with guidelines from causal data science. To this end,…
Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry
Houssam Razouk, Leonie Benischke, Daniel Garber +1
The extraction of causal information from textual data is crucial in the industry for identifying and mitigating potential failures, enhancing process efficiency, prompting quality…
Stylometric Watermarks for Large Language Models
Georg Niess, Roman Kern
The rapid advancement of large language models (LLMs) has made it increasingly difficult to distinguish between text written by humans and machines. Addressing this, we propose a n…