This paper describes our submission to the SemEval-2023 for Task 6 on LegalEval:
Understanding Legal Texts. Our submission concentrated on three subtasks: Legal Named …

Numbers are frequently utilized in both our daily narratives and professional documents,
such as clinical notes, scientific papers, financial documents, and legal court orders. The …

In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant
challenges due to the complexity of legal proceedings and the scarcity of expert-annotated …

Predicting legal judgments with reliable confidence is paramount for responsible legal AI
applications. While transformer-based deep neural networks (DNNs) like BERT have …

Law is one of the knowledge domains that are most reliant on textual material. Nowadays,
however, it is very difficult and time-consuming for legal professionals to read, understand …

We propose a comprehensive study of one-stage elicitation techniques for querying a large
pre-trained generative transformer (GPT-3.5-turbo) in the rhetorical role prediction task of …

Large language models (LLMs) have demonstrated remarkable capabilities in pushing the
boundaries of natural language understanding. Nevertheless, the majority of existing open …

The legal domain is a vast and complex field that involves a considerable amount of text
analysis, including laws, legal arguments, and legal opinions. Legal practitioners must …

In recent years, the field of AI & Law has increasingly focused on predicting legal judgments,
particularly in civil cases. While traditional neural network methods are highly effective at …