Legal NLP

NLP: Natural Language Processing

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9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
Jun 25, 2026Stanisław Sójka, Felix Steffek, Matthias GrabmairLegal NLP AI-Assisted Decision Making
School of Computation, Information, and Technology, Technical University of Munich, Germany · Faculty of Law, University of Cambridge
Measuring & Mitigating Over-Alignment for LLMs in Multilingual Criminal Law Courts
Jun 22, 2026Arthur Wuhrmann, Gaetan Stein, Daniel Brunner +1Multilingual Language Model Evaluation Legal NLP
Swiss Federal Supreme Court · HES-SO Valais-Wallis
Peeking Inside LLMs: Leveraging Internal Artifacts of LLMs for Enhancing Reliability in Legal Classification
Jun 18, 2026Sudipta Santra, Debtanu Datta, Saptarshi GhoshLegal NLP Language Model Error Detection
Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India
FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs
Jun 18, 2026Elijah Feldman, Dipak Meher, Carlotta DomeniconiRelation Extraction KG Construction
Thomas Jefferson High School for Science and Technology, Annandale, VA, USA · Department of Computer Science, George Mason University, Fairfax, VA, USA
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
Jun 15, 2026Mingxu Tao, Jiawei Hu, Xian Zhou +5Legal NLP Legal IR
Center of Information Research, AMS · 2Discipline and Technology Research Center for Large Model Intelligence Applications · 3Hebei University of Engineering
Neuron Level Analysis of Large Language Model in Legal Domain Reasoning
Jun 14, 2026Eri Onami, Youmi Ma, Shuhei Kurita +1Legal NLP LLM Interpretability
Institute of Science Tokyo · NII · AIST
LAUKIN: A Multi-jurisdictional Common Law Contract Dataset
Jun 11, 2026Amrita Singh, Aditya Joshi, Jiaojiao Jiang +2Legal NLP Pairwise Comparison
Computer Science and Engineering, UNSW, Sydney, Australia · Law and Justice, UNSW, Sydney, Australia
Detecting Sensitive Personal Information in Japanese Pre-Training Corpora for Large Language Models
Jun 10, 2026Rei Minamoto, Yusuke Oda, Daisuke KawaharaLegal NLP Privacy-Preserving Language Models
Waseda University · Research and Development Center for LLMs, National Institute of Informatics
From Statute to Control Flow: Span-Grounded Deontic Trees for Defeasible Scope Parsing
Jun 8, 2026Jian Chen, Siyuan Li, Chucheng Wan +1Logical Reasoning Legal NLP
The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China · Sun Yat-Sen University Guangzhou, China
HKJudge: A Legal Discourse-Annotated Corpus for Interpreting What Courts Find, How They Reason, and What They Rule
Jun 4, 2026Xi Xuan, Wenxin Zhang, Yufei Zhou +2Legal NLP Document Information Extraction
City University of Hong Kong · University of Chinese Academy of Sciences
EURO-5K: When Does Domain Pretraining Matter? Benchmarking Transformers for EU Reporting Obligation Extraction
Jun 2, 2026Marios Koniaris, Vasileios Kotronis, Eugenia Giannini +1Language Model Pretraining Legal NLP
Division of Computer Science, School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, Zographou Campus, 15780, Athens, Greece. · Department of Humanities Social Sciences and Law, School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Iroon Polytechniou 9, Zographou Campus, 15780, Athens, Greece.
On Wednesdays, We Ask Questions: Optimizing "Active Listening" in Automated Legal Triage and Referral
May 29, 2026Quinten Steenhuis, Jacqueline HarveyLegal NLP AI-Assisted Decision Making
Suffolk University Law School Boston, United States of America
Bundesrecht: An Open Library and Corpus for German Statutory Reference Processing
May 29, 2026Harshil Darji, Martin Heckelmann, Christina Kratsch +1Legal NLP Legal Document Analysis
Hochschule für Technik und Wirtschaft Berlin, Germany · Hasso-Plattner Institute / University of Potsdam, Germany
Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions
May 28, 2026Volodymyr OvcharovMultilingual Language Model Evaluation Legal NLP
SecondLayer
UA-Legal-Bench: A Benchmark for Evaluating Large Language Models on Ukrainian Legal Reasoning
May 27, 2026Volodymyr OvcharovMultilingual Language Model Evaluation Legal NLP
SecondLayer
BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
May 27, 2026Sebastian Nagl, Ann-Kristin Mayrhofer, Martin Heidebach +6Legal NLP LLM Evaluation
Technical University of Munich (TUM) · Ludwig Maximilian University of Munich (LMU) · University of Konstanz +1
Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service
May 25, 2026Christoffer Loeffler, Tomás Rey Pizarro, Daniel Ignacio Miranda Vásquez +1Retrieval-Augmented Generation Legal NLP
School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Brasil 2950, Valparaíso, 2340025, Chile. · Faculty of Law, Universidad Adolfo Ibáñez, Av. Padre Hurtado 750,2026 Viña del Mar, 2581793, Chile.
TypedCSIP: Typed Counterfactual Pretraining for Chinese Legislative Conflict Classification
May 25, 2026Yao LiuLegal NLP Classification
Chengdu University of Technology, Leshan, China
Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions
May 23, 2026Volodymyr OvcharovLanguage Model Pretraining Legal NLP
LP-Eval: Rubric and Dataset for Measuring the Quality of Legal Proposition Generation
May 19, 2026Shanshan Xu, Johan Lindholm, Amogh Raina +2Legal NLP LLM Evaluation
University of Copenhagen · Umeå University
Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
May 17, 2026M. Mikail Demir, M. Abdullah CanbazLegal NLP LLM Evaluation
Department of Information Science and Technology · College of Emergency Preparedness, Homeland Security, and Cybersecurity · University at Albany, SUNY
Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
May 16, 2026Li Zhang, Jaromir Savelka, Kevin AshleyLegal NLP Multi-Label Classification
University of Pittsburgh, Pittsburgh, PA, USA · Carnegie Mellon University, Pittsburgh, PA, USA
Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Study
May 14, 2026Volodymyr OvcharovMultilingual Language Model Evaluation Legal NLP
LEX AI Platform, legal.org.ua Kyiv, Ukraine
LLMs as annotators of credibility assessment in Danish asylum decisions: evaluating classification performance and errors beyond aggregated metrics
May 13, 2026Galadrielle Humblot-Renaux, Mohammad N. S. Jahromi, Rohat Bakuri-Jørgensen +7Legal NLP LLM Evaluation
Visual Analysis and Perception Lab, Aalborg University · Pioneer Center for AI, Denmark · Center of Excellence for Global Mobility Law, University of Copenhagen +1
Magis-Bench: Evaluating LLMs on Magistrate-Level Legal Tasks
May 8, 2026Ramon Pires, Thales Sales Almeida, Celio Larcher Junior +6Legal NLP LLM Evaluation
Maritaca AI Campinas, São Paulo, Brazil · Jusbrasil Salvador, Bahia, Brazil
Generating training datasets for legal chatbots in Korean
May 8, 2026Changhoe Hwang, Jee-Sun Nam, Eric LaporteLegal NLP Synthetic Data Generation
Hankuk University of Foreign Studies, DICORA · Univ Gustave Eiffel, CNRS, LIGM
A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction
May 7, 2026Nicole Lincoln, Nick Whitehouse, Jaron Mar +1Legal NLP Mixture-of-Experts Language Models
Onit AI Labs, Onit Inc., New Zealand
LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model
Apr 28, 2026Youngjoon Jang, Chanhee Park, Hyeonseok Moon +5Legal NLP LLM Fine-Tuning
Department of Computer Science and Engineering, Korea University · KT (Korea Telecom)
Faithful Autoformalization via Roundtrip Verification and Repair
Apr 27, 2026Daneshvar Amrollahi, Jerry Lopez, Clark BarrettLLM Self-Correction Legal NLP
Stanford University, USA
DWTSumm: Discrete Wavelet Transform for Document Summarization
Apr 22, 2026Rana Salama, Abdou Youssef, Mona DiabLegal NLP LLM Grounding
School of Engineering and Applied Science, George Washington University, USA · Language Technologies Institute, Carnegie Mellon University, USA

Jun 25, 2026, cs.CLJ/K move · Enter open · S save

Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning

Stanisław Sójka, Felix Steffek, Matthias Grabmair

School of Computation, Information, and Technology, Technical University of Munich, Germany · Faculty of Law, University of Cambridge

Legal NLP AI-Assisted Decision Making Legal Judgment Prediction Interpretable ML Multi-Task Learning Legal Reasoning

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Legal outcome prediction must disentangle objective case facts from adjudicative context. Merit-based rulings rely on factual evidence while technical disposals may hinge on judicial discretion. We propose a Judge-Aware Gated Multi-Task Learning architecture that explicitly models this distinction. We introduce a fine-grained outcome taxonomy to supervise the encoder, enforcing a structural regularization that disentangles distinct semantic pathways. This granular legal curriculum enables our Gated Fusion mechanism to dynamically modulate reliance on judge identity. We evaluate our approach on 13,937 UK Employment Tribunal decisions. We benchmark our design against supervised fine-tuning (SFT) of a Gemma-4 26B-A4B backbone, in which judge identity and the taxonomy are injected as prompt tokens or autoregressive output targets. The two contextual signals compose only weakly when forced through a single autoregressive channel. In contrast, coupling a LoRA-adapted Gemma-4 encoder with our gated architecture defines a new state of the art on this benchmark while requiring an order of magnitude fewer trainable parameters than the generative SFT baselines, with gains concentrated on the most ambiguous and rarest outcome classes. Beyond accuracy, the architecture is interpretable; learned judge embeddings and calibration profiles localize the cases where adjudicative context drives the prediction. These results indicate that, for identity-conditioned classification of legal outcomes, the choice of conditioning interface dominates scale: differentiable structured composition yields more accurate, more parameter-efficient models than prompt-based composition over a substantially larger backbone.

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