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cs.CL2024
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,…
cs.CL2024
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…
cs.CL2024
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…