Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey

TL;DR

This paper addresses how large language models can be integrated into legal systems, outlining tasks, risks, and data resources. It provides a comprehensive survey of applications in legal text processing, case retrieval, education, and practice, along with challenges such as bias, hallucination, and interpretability, and the datasets that enable domain-adapted LLMs. The authors develop a taxonomy of methods (prompting, fine-tuning, retrieval augmentation, and hybrid reasoning) and summarize regional efforts to tailor LLMs for specific legal systems. The work offers a practical roadmap for responsible deployment, underscoring human-in-the-loop oversight and the need for standardized data, benchmarks, and governance to improve reliability and justice outcomes.

Abstract

With the advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), there is a profound transformation occurring in the realm of natural language processing tasks within the legal domain. The capabilities of LLMs are increasingly demonstrating unique roles in the legal sector, bringing both distinctive benefits and various challenges. This survey delves into the synergy between LLMs and the legal system, such as their applications in tasks like legal text comprehension, case retrieval, and analysis. Furthermore, this survey highlights key challenges faced by LLMs in the legal domain, including bias, interpretability, and ethical considerations, as well as how researchers are addressing these issues. The survey showcases the latest advancements in fine-tuned legal LLMs tailored for various legal systems, along with legal datasets available for fine-tuning LLMs in various languages. Additionally, it proposes directions for future research and development.