Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models

TL;DR

This paper evaluates modern AI models, especially GPT, for legal question-answering in India, highlighting their strengths, challenges, and potential in handling complex legal queries within the Indian criminal law context.

Contribution

It provides a comparative analysis of AI frameworks for Indian legal QA, emphasizing empirical evaluation and expert insights to assess AI's effectiveness and challenges.

Findings

AI models effectively interpret natural language prompts.

Prevailing AILQA systems produce accurate legal responses.

Challenges include handling complex legal nuances and logistical constraints.

Abstract

Legal QA platforms bear the promise to metamorphose the manner in which legal experts engage with jurisprudential documents. In this exposition, we embark on a comparative exploration of contemporary AI frameworks, gauging their adeptness in catering to the unique demands of the Indian legal milieu, with a keen emphasis on Indian Legal Question Answering (AILQA). Our discourse zeroes in on an array of retrieval and QA mechanisms, positioning the OpenAI GPT model as a reference point. The findings underscore the proficiency of prevailing AILQA paradigms in decoding natural language prompts and churning out precise responses. The ambit of this study is tethered to the Indian criminal legal landscape, distinguished by its intricate nature and associated logistical constraints. To ensure a holistic evaluation, we juxtapose empirical metrics with insights garnered from seasoned legal practitioners, thereby painting a comprehensive picture of AI's potential and challenges within the realm of Indian legal QA.