most citedPost-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts

76 citations · 76 across the 1 of their papers we have counts for

collaborators

5 papers

cs.LG20241 cited

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.…

cs.LG2024

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…

stat.ML20232 cited

LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs

Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori +4

We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components. By…

cs.CL202331 cited

ChatGPT Participates in a Computer Science Exam

Sebastian Bordt, Ulrike von Luxburg

We asked ChatGPT to participate in an undergraduate computer science exam on ''Algorithms and Data Structures''. The program was evaluated on the entire exam as posed to the studen…

cs.LG202276 cited

Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts

Sebastian Bordt, Michèle Finck, Eric Raidl +1

Existing and planned legislation stipulates various obligations to provide information about machine learning algorithms and their functioning, often interpreted as obligations to…