43 citations · 47 across the 7 of their papers we have counts for
8 papers
SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals
Jingyun Jia, Antoine Remond-Tiedrez, Aaron Alvarez +3
Evaluating statistical discovery by large language model (LLM) agents requires verifiable analytical ground truth. Establishing such ground truth for real-world datasets is costly,…
Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models
Jingyun Jia, Chandan Singh, Rich Caruana +1
Identifying meaningful feature interactions is a central challenge in building accurate and interpretable models for tabular data. Generalized additive models (GAMs) have shown gre…
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…
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.…
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
Sebastian Bordt, Harsha Nori, Rich Caruana
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.…
Data Science with LLMs and Interpretable Models
Sebastian Bordt, Ben Lengerich, Harsha Nori +1
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans. In this work, we sho…
Rethinking Interpretability in the Era of Large Language Models
Chandan Singh, Jeevana Priya Inala, Michel Galley +2
Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously…
Explaining high-dimensional text classifiers
Odelia Melamed, Rich Caruana
Explainability has become a valuable tool in the last few years, helping humans better understand AI-guided decisions. However, the classic explainability tools are sometimes quite…