A Short Survey of Viewing Large Language Models in Legal Aspect

TL;DR

This survey reviews the application of large language models in legal tasks, discussing their benefits, challenges like bias and privacy, and future research directions in integrating LLMs into the legal domain.

Contribution

It provides a comprehensive overview of how LLMs are used in law, analyzes legal challenges, and discusses data resources for legal domain adaptation.

Findings

LLMs are increasingly used for legal judgment prediction and document analysis.

Legal challenges include privacy, bias, and explainability issues.

Future directions involve addressing legal challenges and improving domain-specific LLMs.

Abstract

Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a significant impact in the field of law, where they are being increasingly utilized to automate various legal tasks, such as legal judgement prediction, legal document analysis, and legal document writing. However, the integration of LLMs into the legal field has also raised several legal problems, including privacy concerns, bias, and explainability. In this survey, we explore the integration of LLMs into the field of law. We discuss the various applications of LLMs in legal tasks, examine the legal challenges that arise from their use, and explore the data resources that can be used to specialize LLMs in the legal domain. Finally, we discuss several promising directions and conclude this paper. By doing so, we hope to provide an overview of the current state of LLMs in law and highlight the potential benefits and challenges of their integration.