Scott Lundberg

Understanding Global Feature Contributions With Additive Importance Measures

2020 · arXiv (Cornell University)

Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability. To assess the role of individual input features in a global sense, we …

Explaining a Series of Models by Propagating Shapley Values

2021 · arXiv (Cornell University)

Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of …

ART: Automatic multi-step reasoning and tool-use for large language models

2023 · arXiv (Cornell University)

Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning step can rely on external tools to support computation beyond …