How desirable is alignment between LLMs and linguistically diverse human users?

We discuss how desirable it is that Large Language Models (LLMs) be able to adapt or align their language behavior with users who may be diverse in their language use. User diversity may come about among others due to i) age differences; ii) gender characteristics, and/or iii) multilingual experience, and associated differences in language processing and use. We consider potential consequences for usability, communication, and LLM development.

Extending Information Bottleneck Attribution to Video Sequences

We introduce VIBA, a novel approach for explainable video classification by adapting Information Bottlenecks for Attribution (IBA) to video sequences. While most traditional explainability methods are designed for image models, our IBA framework addresses the need for explainability in temporal models used for video analysis. To demonstrate its effectiveness, we apply VIBA to video deepfake detection, testing it on two architectures: the Xception model for spatial features and a VGG11-based model for capturing motion dynamics through optical flow. Using a custom dataset that reflects recent deepfake generation techniques, we adapt IBA to create relevance and optical flow maps, visually highlighting manipulated regions and motion inconsistencies. Our results show that VIBA generates temporally and spatially consistent explanations, which align closely with human annotations, thus providing interpretability for video classification and particularly for deepfake detection.

Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection

Many European citizens become targets of the Kremlin propaganda campaigns, aiming to minimise public support for Ukraine, foster a climate of mistrust and disunity, and shape elections (Meister, 2022). To address this challenge, we developed ''Check News in 1 Click'', the first NLP-empowered pro-Kremlin propaganda detection application available in 7 languages, which provides the lay user with feedback on their news, and explains manipulative linguistic features and keywords. We conducted a user study, analysed user entries and models' behaviour paired with questionnaire answers, and investigated the advantages and disadvantages of the proposed interpretative solution.

Verbreitungsmechanismen schädigender Sprache im Netz: Anatomie zweier Shitstorms

In this working paper, we turn our attention to two exemplary, cross-media shitstorms directed against well-known individuals from the business world. Both have in common, first, the trigger, a controversial statement by the person who thereby becomes the target of the shitstorm, and second, the identity of this target as relatively privileged: cis-male, white, successful. We examine the spread of the outrage wave across two media at a time and test the applicability of computational linguistic methods for analyzing its time course. Assuming that harmful language spreads like a virus in digital space, we are primarily interested in the events and constellations that lead to the use of harmful language, and whether and how a linguistic formation of "tribes" occurs. Our research therefore focuses, first, on the distribution of linguistic features within the overall shitstorm: are individual words or phrases increasingly used after their introduction, and through which pathways they spread. Second, we ask whether "tribes," for example, one group of supporters and one of opponents of the target, have a distinguished linguistic form. Our hypothesis is that supporters remain equally active over time, while the dynamic "ripple" effect of the shitstorm is based on the varying participation of opponents.

PapagAI:Automated Feedback for Reflective Essays

Written reflective practice is a regular exercise pre-service teachers perform during their higher education. Usually, their lecturers are expected to provide individual feedback, which can be a challenging task to perform on a regular basis. In this paper, we present the first open-source automated feedback tool based on didactic theory and implemented as a hybrid AI system. We describe the components and discuss the advantages and disadvantages of our system compared to the state-of-art generative large language models. The main objective of our work is to enable better learning outcomes for students and to complement the teaching activities of lecturers.

Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts

The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data reflecting opposing ideologies and narratives. These polarized campaigns have led to mutual accusations of misinformation and fake news, shaping an atmosphere of confusion and mistrust for readers worldwide. This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian and English. We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features. We analyse the advantages and disadvantages of both methods, their adaptability to new genres and languages, and ethical considerations of their usage for content moderation. With this work, we aim to lay the foundation for further development of moderation tools tailored to the current conflict.