  \emph{Circuit analysis} is a promising technique for understanding the
internal mechanisms of language models. However, existing analyses are done in
small models far from the state of the art. To address this, we present a case
study of circuit analysis in the 70B Chinchilla model, aiming to test the
scalability of circuit analysis. In particular, we study multiple-choice
question answering, and investigate Chinchilla's capability to identify the
correct answer \emph{label} given knowledge of the correct answer \emph{text}.
We find that the existing techniques of logit attribution, attention pattern
visualization, and activation patching naturally scale to Chinchilla, allowing
us to identify and categorize a small set of `output nodes' (attention heads
and MLPs).
  We further study the `correct letter' category of attention heads aiming to
understand the semantics of their features, with mixed results. For normal
multiple-choice question answers, we significantly compress the query, key and
value subspaces of the head without loss of performance when operating on the
answer labels for multiple-choice questions, and we show that the query and key
subspaces represent an `Nth item in an enumeration' feature to at least some
extent. However, when we attempt to use this explanation to understand the
heads' behaviour on a more general distribution including randomized answer
labels, we find that it is only a partial explanation, suggesting there is more
to learn about the operation of `correct letter' heads on multiple choice
question answering.
