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Understanding and explaining legal systems is very challenging due to their complex structure, specialized terminology, and multiple interpretations. Legal AI models are currently undergoing drastic advancements due to the development of Large Language Models (LLMs) that have achieved state-of-the-art performance on a wide range of tasks and are currently undergoing very rapid iterations. As an emerging field, the application of LLMs in the legal field is still in its early stages, with multiple challenges that need to be addressed. Our objective is to provide a comprehensive survey of legal LLMs, not only reviewing the models themselves but also analyzing their applications within the legal systems in different geographies. The paper begins by providing a high-level overview of AI technologies in the legal field and showcasing recent research advancements in LLMs, followed by practical implementations of legal LLMs. Two databases (i.e., SCOPUS and Web of Science) were considered alongside additional related studies that met our selection criteria. We used the PRISMA for Scoping Reviews (PRISMA-ScR) guidelines as the methodology to extract relevant studies and report our findings. The paper discusses and analyses the limitations and challenges faced by legal LLMs, including issues related to data, algorithms, and judicial practices. Moreover, we examine the extent to which such systems can be effectively deployed. The paper summarizes recommendations and future directions to address challenges, aiming to help stakeholders overcome limitations and integrate legal LLMs into the judicial system.
Padiu et al. (Tue,) studied this question.