6 papers
QUAIL: Quantization Aware Unlearning for Mitigating Misinformation in LLMs
Himanshu Mishra, Kanwal Mehreen
Machine unlearning aims to remove specific knowledge (e.g., copyrighted or private data) from a trained model without full retraining. In practice, models are often quantized (e.g.…
Alif: Advancing Urdu Large Language Models via Multilingual Synthetic Data Distillation
Muhammad Ali Shafique, Kanwal Mehreen, Muhammad Arham +3
Developing a high-performing large language models (LLMs) for low-resource languages such as Urdu, present several challenges. These challenges include the scarcity of high-quality…
Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering
Karthik Menon, Batool Arhamna Haider, Muhammad Arham +3
This study introduces Query Attribute Modeling (QAM), a hybrid framework that enhances search precision and relevance by decomposing open text queries into structured metadata tags…
Robust and Fine-Grained Detection of AI Generated Texts
Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen +11
An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existence day by day. Existing systems often…
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Siddhant Gupta +6
Large Language Models (LLMs) have shown remarkable capabilities, but their development has primarily focused on English and other high-resource languages, leaving many languages un…
Augmenting Legal Decision Support Systems with LLM-based NLI for Analyzing Social Media Evidence
Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen +2
This paper presents our system description and error analysis of our entry for NLLP 2024 shared task on Legal Natural Language Inference (L-NLI) \citep{hagag2024legallenssharedtask…