6 papers
Reasoning Isn't Enough: Examining Truth-Bias and Sycophancy in LLMs
Emilio Barkett, Olivia Long, Madhavendra Thakur
Despite their widespread use in fact-checking, moderation, and high-stakes decision-making, large language models (LLMs) remain poorly understood as judges of truth. This study pre…
Governing Automated Strategic Intelligence
Nicholas Kruus, Madhavendra Thakur, Adam Khoja +24
Military and economic strategic competitiveness between nation-states will increasingly be defined by the capability and cost of their frontier artificial intelligence models. Amon…
Culturally-Grounded Chain-of-Thought (CG-CoT):Enhancing LLM Performance on Culturally-Specific Tasks in Low-Resource Languages
Madhavendra Thakur
Large Language Models (LLMs) struggle with culturally-specific reasoning tasks, particularly in low-resource languages, hindering their global applicability. Addressing this gap is…
Opportunities and Challenges of Frontier Data Governance With Synthetic Data
Madhavendra Thakur, Jason Hausenloy
Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance…
Towards Neural No-Resource Language Translation: A Comparative Evaluation of Approaches
Madhavendra Thakur
No-resource languages - those with minimal or no digital representation - pose unique challenges for machine translation (MT). Unlike low-resource languages, which rely on limited…
Towards Data Governance of Frontier AI Models
Jason Hausenloy, Duncan McClements, Madhavendra Thakur
Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primaril…