AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
Abstract: This paper evaluates artificial intelligence models for answering legal questions within the Indian legal system, focusing on \textbf{A}rtificial Intelligence for \textbf{I}ndian \textbf{L}egal \textbf{Q}uestion \textbf{A}nswering \textbf{\texttt{AILQA}}. Utilizing the OpenAI GPT model as a benchmark, we explore the performance of various AI-driven QA algorithms. Our findings highlight the high accuracy of \texttt{AILQA} systems in interpreting natural language queries and generating responses, especially within the complex Indian criminal justice domain. We also present a comprehensive evaluation methodology to assess these systems rigorously. Feedback from legal professionals enriches our analysis, providing insights into the practical applications and limitations of AI in legal QA. The study underscores the need for ongoing research and careful selection of AI models to enhance the efficacy of legal QA systems in India.
Paper Type: Short
Research Area: Question Answering
Research Area Keywords: Question Answering, Legal NLP, Information Extraction, Machine Learning for NLP, Dialogue and Interactive Systems, Resources and Evaluation
Contribution Types: NLP engineering experiment, Reproduction study, Publicly available software and/or pre-trained models, Data resources
Languages Studied: English
Submission Number: 495
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