  Probing strategies have been shown to detect the presence of various
linguistic features in large language models; in particular, semantic features
intermediate to the "natural logic" fragment of the Natural Language Inference
task (NLI). In the case of natural logic, the relation between the intermediate
features and the entailment label is explicitly known: as such, this provides a
ripe setting for interventional studies on the NLI models' representations,
allowing for stronger causal conjectures and a deeper critical analysis of
interventional probing methods. In this work, we carry out new and existing
representation-level interventions to investigate the effect of these semantic
features on NLI classification: we perform amnesic probing (which removes
features as directed by learned linear probes) and introduce the mnestic
probing variation (which forgets all dimensions except the probe-selected
ones). Furthermore, we delve into the limitations of these methods and outline
some pitfalls have been obscuring the effectivity of interventional probing
studies.

Computation and Language