Legal Question-Answering in the Indian Context: Efficacy, Challenges,
and Potential of Modern AI Models
- URL: http://arxiv.org/abs/2309.14735v2
- Date: Mon, 16 Oct 2023 04:40:18 GMT
- Title: Legal Question-Answering in the Indian Context: Efficacy, Challenges,
and Potential of Modern AI Models
- Authors: Shubham Kumar Nigam, Shubham Kumar Mishra, Ayush Kumar Mishra, Noel
Shallum and Arnab Bhattacharya
- Abstract summary: Legal QA platforms bear the promise to metamorphose the manner in which legal experts engage with jurisprudential documents.
Our discourse zeroes in on an array of retrieval and QA mechanisms, positioning the OpenAI GPT model as a reference point.
The ambit of this study is tethered to the Indian criminal legal landscape, distinguished by its intricate nature and associated logistical constraints.
- Score: 3.552993426200889
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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