Generative Language Model for the Judiciary (GSJ)

Funding: German Federal and State Ministries of Justice

Partners: University of Cologne (annotation)

Status: Active

The project “Generatives Sprachmodell der Justiz” (GSJ) aims to develop a generative language model. It was initiated in early 2023 with the goal of training a language model on judicial data that has not previously been publicly available.

We aim to develop a model that can be operated locally, on the computing infrastructure of the German state justice administrations. Court files contain highly sensitive personal data that cannot be handed to external inference providers, which rules out the commercial API models that dominate current legal AI practice. The choice of open-weights models, the pseudonymization pipeline, the training setup etc. follows from that constraint.

The work divides into three pillars: making judicial data usable for model development in the first place, building an agentic-driven application that lets a model work with electronic case files (eAkte) the way a judge does, and training a model for the legal tasks that this work involves. Annotation of the released case files is carried out under the direction of our project partners at the University of Cologne.

References

2026

  1. AppeaLLM: Mimicking a Closed-World Environment for Court Decision Prediction
    Niklas Wais, Max Prior, and Runsheng Chen
    In Proceedings of the Symposium on Computer Science and Law (CSLAW ’26), 2026
  2. BenGER: A Collaborative Web Platform for End-to-End Benchmarking of German Legal Tasks
    Sebastian Nagl, and Matthias Grabmair
    In Proceedings of the 21st International Conference on Artificial Intelligence and Law (ICAIL 2026), 2026
    System demonstration
  3. Exploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization
    Mohamed Hesham Elganayni, Runsheng Chen, Sebastian Nagl, and Matthias Grabmair
    In Proceedings of the 21st International Conference on Artificial Intelligence and Law (ICAIL 2026), 2026
  4. CaseArc: Benchmarking End-to-End Legal Timeline Generation
    Laetitia Wedel, and Niklas Wais
    In Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL) at ICAIL 2026, 2026
    Short paper
  5. BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
    Sebastian Nagl, Ann-Kristin Mayrhofer, Martin Heidebach, Aleyna Koçak, Anne Zettelmeier, Elly Breu, Angelina Greiner, Sofija Milijas, and Matthias Grabmair
    In Findings of the Association for Computational Linguistics: EMNLP 2026, 2026
    Accepted; preprint arXiv:2605.28183
  6. Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering
    Max Prior, Andreas Schultz, and Matthias Grabmair
    In Proceedings of the 21st International Conference on Artificial Intelligence and Law (ICAIL 2026), 2026
  7. Chunking German Legal Code
    Max Prior, Natalia Milanova, and Andreas Schultz
    In Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL) at ICAIL 2026, 2026
  8. Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA
    Mohamed Hesham Elganayni, and Selim Saleh
    In Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL) at ICAIL 2026, 2026

2025

  1. CourtPressGER: A German Court Decision to Press Release Summarization Dataset
    S. Nagl, M. H. Elganayni, M. Pospisil, and Matthias Grabmair
    2025
    arXiv preprint; presented and to be published in Jurix 2025 AIDA2J Workshop Proceedings
  2. Learning from Computer Vision: The Effects of Loss Functions on Legal Text Classification with Class Imbalance
    N. Wais, and Matthias Grabmair
    In International Conference on Artificial Intelligence and Law (ICAIL), 2025