Data Science with LLMs and Interpretable Models
Sebastian Bordt Ben Lengerich Harsha Nori Rich Caruana
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 show that large language models (LLMs) are remarkably
good at working with interpretable models, too. In particular, we show that
LLMs can describe, interpret, and debug Generalized Additive Models (GAMs).
Combining the flexibility of LLMs with the breadth of statistical patterns
accurately described by GAMs enables dataset summarization, question answering,
and model critique. LLMs can also improve the interaction between domain
experts and interpretable models, and generate hypotheses about the underlying
phenomenon. We release \url{https://github.com/interpretml/TalkToEBM} as an
open-source LLM-GAM interface.