5 papers
Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs
Ilias Chalkidis, Stephanie Brandl
Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond t…
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations
Stephanie Brandl, Oliver Eberle, Tiago Ribeiro +2
Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP. They are, however, time-consuming and often bi…
On the Interplay between Fairness and Explainability
Stephanie Brandl, Emanuele Bugliarello, Ilias Chalkidis
In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable. Usually these two objectives, fairness and e…
Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models
Laura Cabello, Emanuele Bugliarello, Stephanie Brandl +1
Pretrained machine learning models are known to perpetuate and even amplify existing biases in data, which can result in unfair outcomes that ultimately impact user experience. The…
Rather a Nurse than a Physician -- Contrastive Explanations under Investigation
Oliver Eberle, Ilias Chalkidis, Laura Cabello +1
Contrastive explanations, where one decision is explained in contrast to another, are supposed to be closer to how humans explain a decision than non-contrastive explanations, wher…