Explainable AI techniques for legal sentence classification using transformers

VietLegalLM: Progressive Legal Expertise Through Synthetic Comprehension and Reinforcement Learning

Legal AI systems require accuracy and verifiable reasoning, yet under-resourced languages lack the specialized models needed to meet these standards. This challenge is particularly acute for statute-based civil law systems like Vietnam’s, where the core task is interpreting and applying codified statutes rather than ma...

A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German

Breaking Bureaucracy: Evaluating open-source LLMs for legal document review

In this paper, we evaluate open-source generative LLMs on legal Natural Language Inference (NLI). Legal inspectorial processes take place in specific domains and often deal with confidential data. This creates a need for working with local models that do not require labeled training data. We evaluate our models on the...

Towards Structural Explainability for Reliable and Governable Large Language Models

Let Me Explain!

Text content is the dominant factor in annotation decisions, far outweighing annotator demographics, and that content-focused SHAP explanations are more effective than demographic persona prompting for guiding LLM annotations, showing that explainability methods can improve both the reliability and the transparency of...

Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court

Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the ability of LLMs to classify interpretive canons as articulated by Larenz in the tradition of Savigny. Our contributions are threefold. First, we operationalize this co...