Contribution Analysis of Large Language Models and Data Augmentations for Person Names in Solving Legal Bar Examination at COLIEE 2023

• Published in: The review of socionetwork strategies Vol. 18; no. 1; pp. 123 - 143
• Main Authors: Onaga, Takaaki, Fujita, Masaki, Kano, Yoshinobu
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.04.2024; Springer Nature B.V
• Subjects: Accuracy; Business and Management; Data augmentation; Deep learning; Information Systems Applications (incl.Internet); IT in Business; Large language models; Legal information; Licensing examinations; Machine learning; Recognition; Simulation and Modeling; Predicate argument structure analysis; Question answering; COLIEE 2023; Legal information entailment
• ISSN: 2523-3173; 1867-3236
• DOI: 10.1007/s12626-024-00155-5

This paper describes our system for COLIEE 2023 Task 4, which automatically answers Japanese legal bar exam problems. We propose an extension to our previous system in COLIEE 2022, which achieved the highest accuracy among all submissions using data augmentation. We focus on problems that include mentions of person names. In this paper, we present two main contributions. First, we incorporate LUKE as our deep learning component, which is a named entity recognition model trained on RoBERTa. Second, we fine-tune the pretrained LUKE model in multiple ways, comparing fine-tuning on training datasets that include alphabetical person names and ensembling different fine-tuning models. We confirmed that LUKE and its fine-tuned model on person type problems improve their accuracies. Our formal run results show that LUKE and our fine-tuning approach using alphabetical person names were effective, achieving an accuracy of 0.69 in the COLIEE 2023 Task 4 formal run.