Answering questions related to the legal domain is a complex task, primarily due to the
intricate nature and diverse range of legal document systems. Providing an accurate answer …

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous
improvements across various NLP tasks, and consecutive variants have been proposed to …

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous
improvements across various NLP tasks, and its consecutive variants have been proposed …

Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial
intelligence, especially natural language processing, to benefit tasks in the legal domain. In …

Recent pretraining models in Chinese neglect two important aspects specific to the Chinese
language: glyph and pinyin, which carry significant syntax and semantic information for …

Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of
artificial intelligence, especially natural language processing (NLP). Recently, inspired by …

We propose DISC-LawLLM, an intelligent legal system utilizing large language models
(LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting …

Many specialized domains remain untouched by deep learning, as large labeled datasets
require expensive expert annotators. We address this bottleneck within the legal domain by …

The VNHSGE (VietNamese High School Graduation Examination) dataset, developed
exclusively for evaluating large language models (LLMs), is introduced in this article. The …

The surge in legal text production has amplified the workload for legal professionals, making
many tasks repetitive and time-consuming. Furthermore, the complexity and specialized …