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cs.CL2025
Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language
Kristin Gnadt, David Thulke, Simone Kopeinik +1
In recent years, various methods have been proposed to evaluate gender bias in large language models (LLMs). A key challenge lies in the transferability of bias measurement methods…
cs.CL2025
Listen to the Context: Towards Faithful Large Language Models for Retrieval Augmented Generation on Climate Questions
David Thulke, Jakob Kemmler, Christian Dugast +1
Large language models that use retrieval augmented generation have the potential to unlock valuable knowledge for researchers, policymakers, and the public by making long and techn…