LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India
Dnyanesh Panchal, Aaryan Gole, Vaibhav Narute, Raunak Joshi

TL;DR
This paper introduces LawPal, a retrieval-augmented generation system utilizing vector search for providing accurate, efficient, and comprehensive legal information in India, aiming to improve legal literacy and accessibility.
Contribution
The paper presents a novel RAG-based legal chatbot with a specialized dataset and FAISS vector search, enhancing legal information retrieval and understanding in India.
Findings
Improved retrieval speed and accuracy in legal queries.
Effective handling of ambiguous or complex legal questions.
Enhanced legal accessibility and literacy through the chatbot.
Abstract
Access to legal knowledge in India is often hindered by a lack of awareness, misinformation and limited accessibility to judicial resources. Many individuals struggle to navigate complex legal frameworks, leading to the frequent misuse of laws and inadequate legal protection. To address these issues, we propose a Retrieval-Augmented Generation (RAG)-based legal chatbot powered by vectorstore oriented FAISS for efficient and accurate legal information retrieval. Unlike traditional chatbots, our model is trained using an extensive dataset comprising legal books, official documentation and the Indian Constitution, ensuring accurate responses to even the most complex or misleading legal queries. The chatbot leverages FAISS for rapid vector-based search, significantly improving retrieval speed and accuracy. It is also prompt-engineered to handle twisted or ambiguous legal questions, reducing…
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Taxonomy
TopicsArtificial Intelligence in Law
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
