  Labeling neural network submodules with human-legible descriptions is useful
for many downstream tasks: such descriptions can surface failures, guide
interventions, and perhaps even explain important model behaviors. To date,
most mechanistic descriptions of trained networks have involved small models,
narrowly delimited phenomena, and large amounts of human labor. Labeling all
human-interpretable sub-computations in models of increasing size and
complexity will almost certainly require tools that can generate and validate
descriptions automatically. Recently, techniques that use learned models
in-the-loop for labeling have begun to gain traction, but methods for
evaluating their efficacy are limited and ad-hoc. How should we validate and
compare open-ended labeling tools? This paper introduces FIND (Function
INterpretation and Description), a benchmark suite for evaluating the building
blocks of automated interpretability methods. FIND contains functions that
resemble components of trained neural networks, and accompanying descriptions
of the kind we seek to generate. The functions span textual and numeric
domains, and involve a range of real-world complexities. We evaluate methods
that use pretrained language models (LMs) to produce descriptions of function
behavior in natural language and code. Additionally, we introduce a new
interactive method in which an Automated Interpretability Agent (AIA) generates
function descriptions. We find that an AIA, built from an LM with black-box
access to functions, can infer function structure, acting as a scientist by
forming hypotheses, proposing experiments, and updating descriptions in light
of new data. However, AIA descriptions tend to capture global function behavior
and miss local details. These results suggest that FIND will be useful for
evaluating more sophisticated interpretability methods before they are applied
to real-world models.
