Computational Linguistics
Department at Trier University
The Chair of Computational Linguistics at Trier University has been held since 2019 by Prof Dr Achim Rettinger, who heads the Computational Linguistics team. Computational Linguistics at Trier University focuses on the machine learning of knowledge representation, primarily from textual media. These are used to understand natural language, to analyse media and to integrate and provide knowledge.
The page is currently in development.
The following content is preliminary and will change.
Projects
Exploring the Interplay between Emotions and Distortion in Informal Science Discourse
- Funding
- German Research Foundation (DFG) / French National Research Agency (ANR)
- Duration
- Mar 2026 – Feb 2029
- Contact
- Achim Rettinger
More and more people learn about science informally, through social media, online forums and, increasingly, conversations with AI chatbots. In these settings, scientific findings are often simplified, exaggerated or distorted. Emotions play a key role: content that triggers fear, anger or excitement tends to spread further and can make distorted claims seem more convincing. How emotions and distorted science interact, and how AI systems affect this, is still poorly understood. EMO-SCI is a Franco-German project that studies these interactions in online science discourse. Partners from Heinrich Heine University Düsseldorf, Trier University, the i3S laboratory and the coordinating lab LIRMM are building new datasets, models and tools. These will be used to analyse how emotions drive the spread of scientific misinformation and disinformation, including in exchanges with AI chatbots. The goal is to better understand these effects and to provide tools that help counter scientific disinformation online.
TWin of Online Social Networks
- Funding
- Horizon Europe Research and Innovation Actions
- Duration
- Apr 2023 – Mar 2026
- Contact
- Achim Rettinger
It is a widely shared concern by experts that Online Social Networks (OSNs) have transformed public debate in harmful ways. Personalization algorithms can create filter bubbles and echo chambers which reinforces and polarizes the users’ opinions. Thus, OSNs have been blamed for spreading false information. To this point, tech-companies argue that there is no unequivocal empirical evidence and that they should not interfere with user behavior. The TWON project aims to provide scientific evidence on both counterarguments, whether social networks have undesirable effects on democratic debates and how they inferfere with user behavior. This will be done by creating digital twins of online social networks to estimate the effects of a change in the system.
Team
Achim Rettinger
Chair of Computational Linguistics
Achim Rettinger is a full professor at Trier University, a director at FZI Forschungszentrum Informatik and supervising director at HoP House of Participation. He leads the krAil research group. Prof. Rettinger’s research spans machine learning, natural language understanding, knowledge representation, and the Web of Data. His group’s work has received multiple best paper, demo, challenge, and presentation awards. He has served as a senior Program Committee member of the International Semantic Web Conference (ISWC) and as a track chair and steering committee member of the European Semantic Web Conference (ESWC). He is also a member of the Board of Directors of the AI for Good Foundation.
Nils Schwager
Research Fellow
Nils Schwager is a Research Fellow at the Department of Computational Linguistics at Trier University, where he brings together computational social science and natural language processing. After completing a B.A. in Media and Communication Studies and Political Science, he recognized the potential of computational methods to investigate questions of polarization in societies fragmented by digital mass media and pursued an M.Sc. in Natural Language Processing. Most of his research is in computational social science, much of it together with his colleagues at EVAS.
Publications
Evaluating Token Probabilities as Confidence Estimators in Human Behavior Simulations: A Case Study in Luxembourgish Next Like Prediction
Nils Schwager, Simon Münker, Alistair Plum, Achim Rettinger
W-NUT @ EMNLP, 2026
Project: EMO-SCI
Abstract
As large language models (LLMs) are increasingly used to simulate human behavior, their operational validity is typically assessed through accuracy alone, implicitly treating human behavior as deterministic. We argue that on noisy digital trace data, a model’s performance depends not only on predicting the correct outcome but on its calibration. We present a case study testing whether the token probabilities of open-weight LLMs can serve as confidence estimates in a Next Like Prediction task: predicting how a user of the Luxembourgish news platform RTL.lu rates a comment, conditioned on their prior interactions. Evaluating Ministral 3 (3B, 8B), we find that, with in-context learning only, the tested models are overconfident and are outperformed on accuracy and calibration by a simple statistical baseline. Accuracy and calibration do not move together: scaling from 3B to 8B improves accuracy while degrading calibration, and longer behavioral histories raise accuracy while confidence declines. Task-specific supervised fine-tuning acts as an effective calibration intervention, reducing Expected Calibration Error from 0.2 to 0.006 while improving accuracy from 0.536 to 0.758. We conclude that confidence estimation should be evaluated alongside accuracy to improve validity when using LLM-based agents.
The Unsampled Truth: Quantifying Prompt Artifacts in LM Psychometrics
Nils Schwager, Christoph Hau, Simon Münker, Achim Rettinger
Findings of EMNLP, 2026
Project: EMO-SCI
Abstract
When prompting language models for psychometric assessment, researchers assume that the responses reflect the injected persona and the meaning of the survey item. We test this premise using a diagnostic design that crosses five semantically distinct baseline personas with five semantically equivalent variants of each of four prompt components (persona wording, task instruction, item wording, option symbol). Measuring the 1-Wasserstein distance between the resulting response distributions and partitioning the variation among the five components allows for the separation of target effects from prompt artifacts. We apply the framework to 13 open-weight small language models (0.6B–14B) on the Big Five Inventory and the Short Dark Triad. We find that in most models, the task instruction and option symbol displace response distributions further than paraphrasing the persona description or the item itself. For a substantial share of items, the artifact share of explained variation exceeds 50%; non-semantic changes of the prompt account for more response variation than the baseline personas. Our framework lets researchers quantify these prompt artifacts before interpreting psychometric output.
Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction
Nils Schwager, Simon Münker, Alistair Plum, Achim Rettinger
WASSA @ EACL, 2026
Project: TWON
Abstract
The transition of Large Language Models (LLMs) from exploratory tools to active “silicon subjects” in social science lacks extensive validation of operational validity. This study introduces Conditioned Comment Prediction (CCP), a task in which a model predicts how a user would comment on a given stimulus by comparing generated outputs with authentic digital traces. This framework enables a rigorous evaluation of current LLM capabilities with respect to the simulation of social media user behavior. We evaluated open-weight 8B models (Llama-3.1, Qwen3, Ministral) in English, German, and Luxembourgish language scenarios. By systematically comparing prompting strategies (explicit vs. implicit) and the impact of Supervised Fine-Tuning (SFT), we identify a critical form vs. content decoupling in low-resource settings: while SFT aligns the surface structure of the text output (length and syntax), it degrades semantic grounding. Furthermore, we demonstrate that explicit conditioning (generated biographies) becomes redundant under fine-tuning, as models successfully perform latent inference directly from behavioral histories. Our findings challenge current “naive prompting” paradigms and offer operational guidelines prioritizing authentic behavioral traces over descriptive personas for high-fidelity simulation.
Learning from recorded games: A scoring policy for simulated soccer agents
Fachberichte Informatik, 2005
Project: TWON
Abstract
This paper outlines the implementation of a new scoring policy for the agents of the Simulated Robot Soccer team from the University of Koblenz, called RoboLog. The applied technique is capable of acting in real time in the dynamic environment of the RoboCup Simulation League and uses data obtained from prerecorded soccer games for supervised neural network learning. The benchmark used for testing this approach is the Optimal Scoring Problem stated as finding the point in the goal where the probability of scoring is the highest when the ball is shot to this point in a given situation. Goalshot situations from numerous logfiles are extracted and employed for the training of two independent multi layered perceptrons. Beside the usage as training patterns the gained data is evaluated statistically and provides interesting general insights into goalshots carried out lately in Simulated Robot Soccer.