1 citations · 1 across the 8 of their papers we have counts for
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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…
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation
Keane Ong, Rui Mao, Deeksha Varshney +3
Counterfactual reasoning typically involves considering alternatives to actual events. While often applied to understand past events, a distinct form-forward counterfactual reasoni…
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…