📝 SummarXiv

Abstract

Judicial reasoning in criminal judgments typically consists of three elements: Holding , evidentiary considerations, and subsumption. These elements form the logical foundation of judicial decision-making but remain unstructured in court documents, limiting large-scale empirical analysis. In this study, we design annotation guidelines to define and distinguish these reasoning components and construct the first dedicated datasets from Taiwanese High Court and Supreme Court criminal judgments. Using the bilingual large language model ChatGLM2, we fine-tune classifiers for each category. Preliminary experiments demonstrate that the model achieves approximately 80% accuracy, showing that judicial reasoning patterns can be systematically identified by large language models even with relatively small annotated corpora. Our contributions are twofold: (1) the creation of structured annotation rules and datasets for Holding, evidentiary considerations, and subsumption; and (2) the demonstration that such reasoning can be computationally learned. This work lays the foundation for large-scale empirical legal studies and legal sociology, providing new tools to analyze judicial fairness, consistency, and transparency.