Master's Thesis: Explainability of Transformer Models in Authorship Verification

Background/Motivation: Authorship verification (AV) is used in areas such as forensics, plagiarism detection, and fake news detection to identify the true author of a text. The goal of authorship verification (AV) is to classify whether two or more texts were written by the same author (Y) or not (N). As in most AI fields today, the most powerful models are usually based on transformer architectures. While these continuously achieve new state-of-the-art results, the application of explainability methods is mostly limited to somewhat older models or architectures. This limits the applicability of the latest methods in practice. Objective: The objective of this work is to apply existing explainability methods to newer, more powerful models and, if necessary, adapt them accordingly. Furthermore, the corresponding methods should be extended, if necessary, to be understandable for non-experts as well. This can be achieved, for example, thru visualizations or the automated extraction of the most important input data. Results: The work should illustrate which explainability approaches are suitable for transformer models or can be adapted for them. Additionally, the various explainability approaches should be compared and, if necessary, combined to create an explainability framework that can be applied in practice. As a result, the findings provide both a scientific contribution to the explainability of modern transformer models and a direct practical benefit in the application of the methods.