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Found 12 papers, 8 papers with code
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
We then compare the few-shot learning performance of LLMs on datasets that were seen during training to the performance on datasets released after training.
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
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
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans.
Statistics without Interpretation: A Sober Look at Explainable Machine Learning
In the rapidly growing literature on explanation algorithms, it often remains unclear what precisely these algorithms are for and how they should be used.
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components.
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
Quantifying the perceptual alignment of model gradients via their similarity with the gradients of generative models, we show that off-manifold robustness correlates well with perceptual alignment.
ChatGPT Participates in a Computer Science Exam
We asked ChatGPT to participate in an undergraduate computer science exam on ''Algorithms and Data Structures''.
From Shapley Values to Generalized Additive Models and back
We then show that $n$-Shapley Values, as well as the Shapley Taylor- and Faith-Shap interaction indices, recover GAMs with interaction terms up to order $n$.
The Manifold Hypothesis for Gradient-Based Explanations
We propose a necessary criterion: their feature attributions need to be aligned with the tangent space of the data manifold.
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts
In this paper, we combine legal, philosophical and technical arguments to show that post-hoc explanation algorithms are unsuitable to achieve the law's objectives.
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong structural assumptions on the data generation process.
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
A lower bound quantifies the worst-case hardness of optimally advising a decision maker who is opaque or has access to private information.
