  Neural models have demonstrated remarkable performance across diverse ranking
tasks. However, the processes and internal mechanisms along which they
determine relevance are still largely unknown. Existing approaches for
analyzing neural ranker behavior with respect to IR properties rely either on
assessing overall model behavior or employing probing methods that may offer an
incomplete understanding of causal mechanisms. To provide a more granular
understanding of internal model decision-making processes, we propose the use
of causal interventions to reverse engineer neural rankers, and demonstrate how
mechanistic interpretability methods can be used to isolate components
satisfying term-frequency axioms within a ranking model. We identify a group of
attention heads that detect duplicate tokens in earlier layers of the model,
then communicate with downstream heads to compute overall document relevance.
More generally, we propose that this style of mechanistic analysis opens up
avenues for reverse engineering the processes neural retrieval models use to
compute relevance. This work aims to initiate granular interpretability efforts
that will not only benefit retrieval model development and training, but
ultimately ensure safer deployment of these models.
