Papers by Matthias Grabmair
CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval (2024.lrec-main)
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| Challenge: | Existing methods to assess Statutory Article Retrieval (SAR) are vague and underspecified, resulting in a lack of clarity and a gap between legal expertise and public comprehension. |
| Approach: | They propose a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR) it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. |
| Outcome: | The proposed approach surpasses static methods and can be used to assess the difficulty of the model. |
LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases (2025.findings-naacl)
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| Challenge: | Existing approaches overlook the underlying semantic intent in determining relevance with respect to a query case. |
| Approach: | They propose a method that generates intents in the form of legal concepts from a query case facts and then augments the query with these concepts to enhance models understanding of semantic intent. |
| Outcome: | The proposed approach generates intents in the form of legal concepts and augments the query with these concepts to enhance models understanding of semantic intent that dictates relavance. |
LexAbSumm: Aspect-based Summarization of Legal Decisions (2024.lrec-main)
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| Challenge: | LexAbSumm is a dataset designed for aspect-based summarization of legal documents . it is based on a set of ECtHR fact sheets, and is available for download. |
| Approach: | They propose a dataset designed for aspect-based summarization of legal case decisions . they evaluate abstractive summarizing models tailored for longer documents . |
| Outcome: | The proposed dataset is designed for aspect-based summarization of legal cases . it reveals a challenge in conditioning models to produce aspect-specific summaries . |
The Craft of Selective Prediction: Towards Reliable Case Outcome Classification - An Empirical Study on European Court of Human Rights Cases (2024.findings-emnlp)
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| Challenge: | Existing COC tasks prioritize high task performance over model reliability . however, large models exhibit overconfidence and Monte Carlo dropout methods produce reliable confidence estimates . |
| Approach: | They conduct an empirical investigation into how various design choices affect the reliability of COC models within the framework of selective prediction. |
| Outcome: | The proposed model is able to predict the outcome of a legal case based on the text of the case facts and is compared with other models using a pre-training corpus. |
Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification (2024.acl-long)
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| Challenge: | Existing methods for NLP calibration ignore inherent human label variation (HLV) split votes are a problem in high-stakes domains such as legal and medical decisions . |
| Approach: | They present a case outcome classification dataset with judges' vote distributions and build a taxonomy of disagreement with SV-specific subcategories. |
| Outcome: | The proposed model is compared against a judge vote distribution and assesses the alignment of perceived difficulty between models and humans. |
AQuAECHR: Attributed Question Answering for European Court of Human Rights (2025.findings-acl)
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| Challenge: | LLMs are widely used for information seeking, but their generated responses often suffer from hallucinations, hindering their widespread adoption in high stakes domains such as law. |
| Approach: | They propose to attribute legal question answering to an actual source to improve factuality and verifiability of the answer. |
| Outcome: | The proposed framework improves the factuality and verifiability of legal question answering by combining a dataset from ECHR case law guides with an LLM-based filtering pipeline. |
Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents (2024.lrec-main)
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| Challenge: | Rhetorical Role Labeling (RRL) of legal judgments presents challenges such as inferring sentence roles from context, interrelated roles, limited annotated data, and label imbalance. |
| Approach: | They propose techniques to enhance RRL performance by leveraging knowledge from semantically similar instances. |
| Outcome: | The proposed methods achieve remarkable improvements in challenging macro-F1 scores. |
GenDLN: Evolutionary Algorithm-Based Stacked LLM Framework for Joint Prompt Optimization (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) are increasingly replacing traditional classification and inference models due to their generality, ability to perform a wide range of tasks, and seemingly advanced "reasoning" prompt optimization is a promising alternative to manual/human prompt engineering, but the cost of using LLMs for prompt optimization via commercial APIs remains high. |
| Approach: | They propose an open-source, efficient genetic algorithm-based prompt pair optimization framework that leverages commercial APIs. |
| Outcome: | The proposed approach allows teams with limited resources to efficiently use commercial LLMs for prompt optimization. |
ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks (2024.acl-long)
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| Challenge: | Existing models overlook the temporal dimension in their training process, leading to suboptimal performance over time. |
| Approach: | They propose a training paradigm that trains models on chronological splits, preserving the temporal order of the data. |
| Outcome: | The proposed model fails to fit to recent data, despite continual learning and temporal invariant methods. |
LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law (2025.acl-industry)
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| Challenge: | Recent efforts focus on automatic summarization of individual cases, which condense the content of a single case, making it easier for legal professionals to grasp key points. |
| Approach: | They propose a pipeline to generate multi-case structured reports using entire body of case law on user-specified topics within the European Court of Human Rights. |
| Outcome: | The proposed pipeline generates structured reports that enhance efficient, scalable legal analysis. |
RELexED: Retrieval-Enhanced Legal Summarization with Exemplar Diversity (2025.findings-naacl)
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| Challenge: | Current approaches to legal summarization struggle with content theme deviation and inconsistent writing styles due to the content of the source document. |
| Approach: | They propose a retrieval-augmented framework that utilizes exemplar summaries along with the source document to guide the model. |
| Outcome: | The proposed model outperforms models that do not utilize exemplars and those that rely on similarity-based exemplar selection. |
ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights (2024.lrec-main)
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| Challenge: | Prior case retrieval datasets do not simulate a realistic setting because they use complete case documents while only masking references to prior cases. |
| Approach: | They propose a prior case retrieval dataset based on judgements from the European Court of Human Rights which explicitly separate facts from arguments and exhibit precedential practices. |
| Outcome: | The proposed datasets do not simulate a realistic setting and expose queries to spurious patterns left behind by citation masks, potentially short-circuiting a comprehensive understanding of case facts and legal principles. |
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases (2023.eacl-main)
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| Challenge: | a new method for case outcome classification is being developed for the European Court of Human Rights. |
| Approach: | They propose to use case facts descriptions to classify whether a court finds a violation of conventions. |
| Outcome: | The proposed model improves on single-task and joint models without contrastive loss. |
From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification (2023.emnlp-main)
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| Challenge: | Existing work in explainable COC has been limited to annotations by a single expert. |
| Approach: | They construct a two-level task-independent taxonomy from a dataset obtained from two experts in the domain of international human rights law . they find disagreements stem from underspecification of the legal context . |
| Outcome: | The proposed dataset is the first in legal NLP that focuses on human label variation. |
HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents (2024.findings-emnlp)
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| Challenge: | Existing approaches overlook the varying difficulty levels inherent in legal document discourse styles and rhetorical roles. |
| Approach: | They propose a hierarchical curriculum learning framework for RRL that nests two curricula: Rhetorical Role-level Curriculum (RC) on the outer layer and Document-level curriculum (DC) on inner layer. |
| Outcome: | The proposed framework is based on four legal document datasets and shows that it is complementary to existing models. |
Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases (2024.findings-emnlp)
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| Challenge: | Inspired by the legal doctrine of stare decisis, we explore methods to integrate precedents into LJP models. |
| Approach: | They propose to integrate precedents into legal judgment prediction models by integrating them at inference and during training via a precedent fusion module. |
| Outcome: | The proposed model outperforms models without precedents or with precedents incorporated only at inference on LJP tasks. |
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases (2023.findings-eacl)
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| Challenge: | Legal Judgment Prediction (LJP) is a classification task that uses textual descriptions of case facts as the input. |
| Approach: | They propose to use legal reasoning to map article text to specific case fact text to improve the model's generalization to zero-shot settings. |
| Outcome: | The proposed model outperforms straightforward fact classification and improves zero-shot transfer performance. |
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval (2025.coling-main)
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| Challenge: | Existing methods for Statutory Article Retrieval (SAR) are vague and underspecified . however, a new approach is needed to bridge the gap between legal expertise and public understanding . |
| Approach: | They propose a framework for statutory article retrieval that leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. |
| Outcome: | The proposed framework overcomes the semantic mismatch problem when modeling each query-article pair in isolation. |
CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization (2025.findings-naacl)
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| Challenge: | Recent efforts to produce concise legal summarization have shifted towards abstractive approaches . |
| Approach: | They propose a framework that integrates content selection and planning components to generate coherent summaries based on both the content and the structured plan. |
| Outcome: | The proposed framework shows that it integrates content selection and planning components over entity-centric approaches in the context of legal judgements. |
Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts (2022.emnlp-main)
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| Challenge: | Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals. |
| Approach: | They propose to use domain expertise to identify statistically predictive but legally irrelevant information and adopt adversarial training to prevent it from relying on it. |
| Outcome: | The proposed model aligns better with expert rationales than baseline models . the results are compared with an existing benchmark dataset of human rights cases . |
Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization (2024.naacl-long)
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| Challenge: | a study explores the cross-jurisdictional generalizability of legal case summarization models . fine-tuning on non-target datasets outperforms unsupervised methods, but success depends on similarity between source and target jurisdictions. |
| Approach: | They explore how to effectively summarize legal cases of a target jurisdiction where reference summaries are not available. |
| Outcome: | The proposed model can be generalized across jurisdictions and improve transfer performance. |
VECHR: A Dataset for Explainable and Robust Classification of Vulnerability Type in the European Court of Human Rights (2023.emnlp-main)
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| Challenge: | Existing work on the concept of vulnerability at the European Court of Human Rights (ECtHR) has focused on classification and analysis of textual data. |
| Approach: | They propose to use an expert-annotated multi-label dataset to assess vulnerability in court cases. |
| Outcome: | The proposed model performs poorly on out-of-domain data and shows that it is robust. |
Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance (2022.findings-emnlp)
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| Challenge: | Summarizing legal decisions requires the expertise of law practitioners, which is time- and cost-intensive. |
| Approach: | They propose methods for extracting summarized legal decisions using limited expert annotated data. |
| Outcome: | The proposed models achieve ROUGE scores vis-à-vis expert extracted summaries that match inter-annotator comparisons. |
Query-driven Relevant Paragraph Extraction from Legal Judgments (2024.lrec-main)
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| Challenge: | Legal professionals struggle with navigating lengthy legal judgements to pinpoint information that directly addresses their queries. |
| Approach: | They construct a specialized dataset to extract relevant paragraphs from legal judgements based on query . they assess the performance of current retrieval models in a zero-shot way . |
| Outcome: | The proposed model outperforms the current retrieval models in a zero-shot way and fine-tunes them using various models. |
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset (2024.lrec-main)
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| Challenge: | Using Swiss Judgement Prediction, we evaluate the explainability of state-of-the-art monolingual and multilingual LJP models. |
| Approach: | They propose an occlusion-based approach to evaluate the explainability performance of legal judgement prediction models using Swiss Judgement Prediction, the only available multilingual LJP dataset. |
| Outcome: | The proposed framework allows us to quantify the influence of lower court information on model predictions, exposing current models’ biases. |