Variational Information Pursuit with Large Language and Multimodal
  Models for Interpretable Predictions
Kwan Ho Ryan Chan Aditya Chattopadhyay Benjamin David Haeffele Rene Vidal
Variational Information Pursuit (V-IP) is a framework for making
interpretable predictions by design by sequentially selecting a short chain of
task-relevant, user-defined and interpretable queries about the data that are
most informative for the task. While this allows for built-in interpretability
in predictive models, applying V-IP to any task requires data samples with
dense concept-labeling by domain experts, limiting the application of V-IP to
small-scale tasks where manual data annotation is feasible. In this work, we
extend the V-IP framework with Foundational Models (FMs) to address this
limitation. More specifically, we use a two-step process, by first leveraging
Large Language Models (LLMs) to generate a sufficiently large candidate set of
task-relevant interpretable concepts, then using Large Multimodal Models to
annotate each data sample by semantic similarity with each concept in the
generated concept set. While other interpretable-by-design frameworks such as
Concept Bottleneck Models (CBMs) require an additional step of removing
repetitive and non-discriminative concepts to have good interpretability and
test performance, we mathematically and empirically justify that, with a
sufficiently informative and task-relevant query (concept) set, the proposed
FM+V-IP method does not require any type of concept filtering. In addition, we
show that FM+V-IP with LLM generated concepts can achieve better test
performance than V-IP with human annotated concepts, demonstrating the
effectiveness of LLMs at generating efficient query sets. Finally, when
compared to other interpretable-by-design frameworks such as CBMs, FM+V-IP can
achieve competitive test performance using fewer number of concepts/queries in
both cases with filtered or unfiltered concept sets.