Comparative Analysis of Artificial Intelligence for Indian Legal Question Answering (AILQA) Using Different Retrieval and QA Models

Legal question-answering (QA) systems have the potential to revolutionize the way legal professionals interact with case law documents. This paper conducts a comparative analysis of existing artificial intelligence models for their utility in answering legal questions within the Indian legal system, specifically focusing on Indian Legal Question Answering (AILQA) and our study investigates the efficacy of different retrieval and QA algorithms currently available. Utilizing the OpenAI GPT model as a benchmark, along with query prompts, our investigation shows that existing AILQA systems can automatically interpret natural language queries from users and generate highly accurate responses. This research is particularly focused on applications within the Indian criminal justice domain, which has its own set of challenges due to its complexity and resource constraints. In order to rigorously assess the performance of these models, empirical evaluations are complemented by feedback from practicing legal professionals, thereby offering a multifaceted view on the capabilities and limitations of AI in the context of Indian legal question-answering.