S c h o o l o f E n g i n e e r i n g
Department of
COMPUTER SCIENCE AND ENGINEERING
CLOR-QA: Cross-Lingual Open-Retrieval Question Answering Model with Dynamic Database Integration
Dr. Sonia Khetarpaul, Vedanta Vivek Patil, Mitra Abhi Sura, Shrey Sharma
This research explores a novel solution to address information scarcity and asymmetry - a Cross-Lingual Open retrieval Question Answering (CLOR-QA) system. This model, trained on English, Chinese, Vietnamese, Arabic, and German, facilitates cross-lingual information retrieval, enabling users to access data in languages different from the query language by integrating a cross-lingual approach into the question-answering system. The user can access the multilingual data entered in real time to get prompt responses through the proposed CLOR-QA model. This system utilises Helsinki-NLP model to bridge gaps between different languages.
LawMate: Leveraging Domain-Specific LLMs for the Indian Legal Ecosystem
Aryaman Pattnayak, Adhityanarayan Ramkumar, Sonia Khetarpaul
The Indian legal system, with its intricate framework and diverse socio-cultural nuances, presents significant accessibility and affordability challenges. LawMate is a domain-specific AI solution utilising Large Language Models (LLMs) and advanced Natural Language Processing (NLP) techniques to bridge this gap. By leveraging LowRank Adaptation (LoRA) methodologies and the TÜLU 3 architecture, Law-Mate offers precise, context-aware responses to complex legal queries, specifically tailored to the Indian legal system. LawMate showed better accuracy of 72% on Indian legal queries, which significantly outperformed GPT-4o ( 45%) and Claude 3.5 Sonnet ( 42%). The model showcased the importance of domain-specific fine-tuning for the Indian legal ecosystem.
