3 papers
cs.HC2026
How people use Copilot for Health
Beatriz Costa-Gomes, Pavel Tolmachev, Eloise Taysom +15
We analyze over 500,000 de-identified health-related conversations with Microsoft Copilot from January 2026 to characterize what people ask conversational AI about health. We devel…
cs.LG2026
GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
Andreas Mueller, Julien Siems, Harsha Nori +4
While interpretability is crucial for machine learning applications in safety-critical domains and for regulatory compliance, existing tabular foundation models like TabPFN lack tr…
cs.LG2024
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
Sebastian Bordt, Harsha Nori, Vanessa Rodrigues +2
While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over.…