Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models
This work addresses the problem of automating legal assistance for Indian legal experts, but it is incremental as it focuses on applying existing methods to a specific domain without major innovations.
The study evaluated modern AI models for legal question-answering in the Indian context, finding that existing AILQA paradigms effectively decode natural language prompts and produce precise responses, though specific performance numbers were not provided.
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.