  In this paper, we introduce a comprehensive reformulation of the task known
as Circuit Discovery, along with DiscoGP, a novel and effective algorithm based
on differentiable masking for discovering circuits. Circuit discovery is the
task of interpreting the computational mechanisms of language models (LMs) by
dissecting their functions and capabilities into sparse subnetworks (circuits).
We identified two major limitations in existing circuit discovery efforts: (1)
a dichotomy between weight-based and connection-edge-based approaches forces
researchers to choose between pruning connections or weights, thereby limiting
the scope of mechanistic interpretation of LMs; (2) algorithms based on
activation patching tend to identify circuits that are neither functionally
faithful nor complete. The performance of these identified circuits is
substantially reduced, often resulting in near-random performance in isolation.
Furthermore, the complement of the circuit -- i.e., the original LM with the
identified circuit removed -- still retains adequate performance, indicating
that essential components of a complete circuits are missed by existing
methods.
  DiscoGP successfully addresses the two aforementioned issues and demonstrates
state-of-the-art faithfulness, completeness, and sparsity. The effectiveness of
the algorithm and its novel structure open up new avenues of gathering new
insights into the internal workings of generative AI.
