Large language model (LLM) with retrieve-augmented generation (RAG) for legal case research

Abstract

The integration of generative artificial intelligence (GAI) in various domains has revolutionized traditional practices, and the legal sector is no exception. However, considering the hallucination of GAI, the correctness of generative outcome is not trustworthy. Hence, this paper presents a novel and safe approach to legal consultation through AI chatting bots, aiming to enhance accessibility, efficiency, and accuracy in legal services.

The chatting bot system discussed in this study utilizes natural language processing (NLP) and large language model (LLM) to understand and respond to legal queries from users. By analyzing vast legal databases and case precedents with a state-of-the-art technology: retrieve augmented generation (RAG), the AI chatting bot can provide insightful and contextually relevant information to users, offering guidance on legal matters ranging from contract interpretation to regulatory compliance.

Key features of the AI chatting bot include personalized recommendations based on user input, real-time updates on legal developments, and interactive dialogue capabilities for clarifying complex legal concepts.

Through a comparative analysis with traditional legal consultation methods, this paper analyzes characteristics of AI-powered chatting bots, such as cost-effectiveness, and scalability. Additionally, it explores the potential impact of AI chatting bots on improving access to justice, especially for underserved populations with limited resources for legal assistance.

Overall, this research highlights the transformative potential of AI in the legal domain and underscores the importance of ethical considerations and continuous evaluation in deploying AI solutions for legal consultation.