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.

翻译：法律问答系统有潜力彻底改变法律专业人士与判例文献交互的方式。本文对现有用于印度法律体系内回答法律问题的人工智能模型进行了比较分析，特别聚焦于印度法律问答（AILQA）系统，并研究了当前可用的不同检索与问答算法的效能。以OpenAI GPT模型作为基准，结合查询提示，我们的研究表明现有AILQA系统能够自动解释用户的自然语言查询，并生成高度准确的回答。本研究尤其关注印度刑事司法领域的应用，该领域因其复杂性和资源限制而面临独特挑战。为严格评估这些模型的性能，实证评估辅以执业法律专业人士的反馈，从而对人工智能在印度法律问答情境下的能力与局限性提供了多维度视角。