6 papers
UrduBench: An Urdu Reasoning Benchmark using Contextually Ensembled Translations with Human-in-the-Loop
Muhammad Ali Shafique, Areej Mehboob, Layba Fiaz +2
Recent advances in large language models (LLMs) have led to strong reasoning capabilities; however, evaluating such models in low-resource languages remains challenging due to the…
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…
DSBC : Data Science task Benchmarking with Context engineering
Ram Mohan Rao Kadiyala, Siddhant Gupta, Jebish Purbey +4
Recent advances in large language models (LLMs) have significantly impacted data science workflows, giving rise to specialized data science agents designed to automate analytical t…
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…