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…
Detecting Linguistic Indicators for Stereotype Assessment with Large Language Models
Rebekka Görge, Michael Mock, Héctor Allende-Cid
Social categories and stereotypes are embedded in language and can introduce data bias into Large Language Models (LLMs). Despite safeguards, these biases often persist in model be…
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…