  Large language models (LLMs) have demonstrated remarkable prediction
performance for a growing array of tasks. However, their rapid proliferation
and increasing opaqueness have created a growing need for interpretability.
Here, we ask whether we can automatically obtain natural language explanations
for black box text modules. A "text module" is any function that maps text to a
scalar continuous value, such as a submodule within an LLM or a fitted model of
a brain region. "Black box" indicates that we only have access to the module's
inputs/outputs.
  We introduce Summarize and Score (SASC), a method that takes in a text module
and returns a natural language explanation of the module's selectivity along
with a score for how reliable the explanation is. We study SASC in 3 contexts.
First, we evaluate SASC on synthetic modules and find that it often recovers
ground truth explanations. Second, we use SASC to explain modules found within
a pre-trained BERT model, enabling inspection of the model's internals.
Finally, we show that SASC can generate explanations for the response of
individual fMRI voxels to language stimuli, with potential applications to
fine-grained brain mapping. All code for using SASC and reproducing results is
made available on Github.
