1 citations · 1 across the 2 of their papers we have counts for
3 papers
Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation
Rebekka Görge, Sujan Sai Gannamaneni, Tabea Naeven +10
Textual data used to train large language models (LLMs) exhibits multifaceted bias manifestations encompassing harmful language and skewed demographic distributions. Regulations su…
Do Multilingual Large Language Models Mitigate Stereotype Bias?
Shangrui Nie, Michael Fromm, Charles Welch +7
While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training o…
Developing trustworthy AI applications with foundation models
Michael Mock, Sebastian Schmidt, Felix Müller +6
The trustworthiness of AI applications has been the subject of recent research and is also addressed in the EU's recently adopted AI Regulation. The currently emerging foundation m…