Matthias Grabmair
Professor
Applies natural language processing and machine learning to legal texts, including legal judgment prediction and argument mining.
Matthias Grabmair's lab at TUM works in Natural Language Processing. 52 papers since 2023, 10 at top venues, mostly ACL and Findings of EMNLP. 6 PhD students and 1 master's student. 2 alumni.
At a glance
- papers, 2023–2026
- 14/ year
- at T1 venues
- 19%
- PhD students
- 6
- MS students
- 1
- alumni listed
- 2
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 0 · collecting
- 19 papers2025 · T1 7
- 15 papers2024 · T1 1
- 8 papers2023 · T1 2
T1 venuesOtherVenues: ACL, Findings of EMNLP, LREC, Findings of NAACL, EMNLP
Research focuspaper titles that use each topic's words
- legal31 papers
- prediction5 papers
- applies natural0 papers
- applies0 papers
- mining0 papers
Paper list
From the lab homepage.
202610 papers
- Proceedings of the Symposium on Computer Science and LawAppeaLLM: Mimicking a Closed-World Environment for Court Decision Prediction (opens in a new tab)
- Proceedings of the International Conference on Artificial Intelligence and Law (ICAILAsking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering
- Proceedings of the International Conference on Artificial Intelligence and Law (ICAILBenGER: A Collaborative Web Platform for End-to-End Benchmarking of German Legal Tasks
- Findings of EMNLPBenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
- Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP )By Their Fruits You Will Know Them: Comparing Formalizations of Law by the Decisions They Encode
- Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL) at ICAILCaseArc: Benchmarking End-to-End Legal Timeline Generation
- Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL) at ICAILChunking German Legal Code
- Proceedings of the International Conference on Artificial Intelligence and Law (ICAILExploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization
- Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL) at ICAILRe-Ranking Through an Attribution Lens for Citation Quality in Legal QA
- Proceedings of the Symposium on Computer Science and LawThinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning
202519 papers
- Findings of ACLAQuAECHR: Attributed Question Answering for European Court of Human Rights
- ACL (T1)CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation
- Findings of NAACLCoPERLex: Content Planning with Event-based Representations for Legal Case Summarization
- S. NaglCourtPressGER: A German Court Decision to Press Release Summarization Dataset
- NLLPEfficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
- ACL (T1)Fairness Beyond Performance: Investigating Reliability Disparities Across Groups in Legal NLP
- ACL WorkshopGenDLN: Evolutionary Algorithm-Based Stacked LLM Framework for Joint Prompt Optimization
- AMELRGenerating Legal Commentaries from Case Databases via Retrieval, Clustering, and Generation
- Künstliche Intelligenz und RechtKI-gestützte Gesetzgebung
- ICAILLearning from Computer Vision: The Effects of Loss Functions on Legal Text Classification with Class Imbalance
- Findings of NAACLLeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights Cases
- ACL (T1)LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments
202415 papers
- Digitalisierung des RechtsAnnotation von Referenzdaten zur Entwicklung einer KI-basierten Entscheidungshilfe für den Europäischen Gerichtshof für Menschenrechte: Ein Werkstattbericht
- NAACL (T1)Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization
- LRECCuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval
- LRECECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights
- Findings of EMNLPHiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents
- Findings of EMNLPIncorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases
- T. Y. S. S. SantoshLexAbSumm: Aspect-based Summarization of Legal Decisions
- Natural Legal Language Processing (NLLP) at EMNLPLexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English
- LRECMind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents
- Proceedings of the Fifth Workshop on Privacy in Natural Language Processing (PrivateNLP) at ACLPrivaT5: A Generative Language Model for Privacy Policies
- LRECQuery-driven Relevant Paragraph Extraction from Legal Judgments
- Rethinking TaxSteuergesetze der Zukunft – digitaltauglich formuliert und digital anwendbar: Ein Werkstattbericht zum zweiten Hackathon zur Digitalisierung des Steuerrechts
20238 papers
- Rethinking TaxDigitaltaugliches Steuerrecht – Werkstattbericht "Hackathon"
- EMNLP (T1)From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification
- ECIRJoint Span Segmentation and Rhetorical Role Labelling with Data Augmentation for Legal Documents
- EACLLeveraging task dependency and contrastive learning for Legal Judgement Prediction on the European Court of Human Rights
- Matthias GrabmairLow Code im Praxistest – Digitaltaugliche Steuergesetzgebung ist notwendig
- Stichwortkommentar Legal TechNatural Language Processing; Dokumentenanalyse
- EMNLP (T1)VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights
- Findings of EACLZero Shot Transfer of Legal Judgement Prediction as Article-aware Entailment for the European Court of Human Rights
Where alumni went2 listed
Apply to TUMRolling
Not announced yet
Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
- —
- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
- See program page
Confirmed on the program page.
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