3 citations · 3 across the 4 of their papers we have counts for
5 papers
ORION: Teaching Language Models to Reason Efficiently in the Language of Thought
Kumar Tanmay, Kriti Aggarwal, Paul Pu Liang +1
Large Reasoning Models (LRMs) achieve strong performance in mathematics, code generation, and task planning, but their reliance on long chains of verbose "thinking" tokens leads to…
Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
Jaedong Hwang, Kumar Tanmay, Seok-Jin Lee +5
Large Language Models (LLMs) have achieved strong performance in domains like mathematics, factual question answering, and code generation, yet their ability to reason on these tas…
Language Models' Factuality Depends on the Language of Inquiry
Tushar Aggarwal, Kumar Tanmay, Ayush Agrawal +3
Multilingual language models (LMs) are expected to recall factual knowledge consistently across languages, yet they often fail to transfer knowledge between languages even when the…
Efficient Conditional Pre-training for Transfer Learning
Shuvam Chakraborty, Burak Uzkent, Kumar Ayush +3
Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This st…
Geography-Aware Self-Supervised Learning
Kumar Ayush, Burak Uzkent, Chenlin Meng +4
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application…