What Changed? Converting Representational Interventions to Natural
  Language
Matan Avitan Ryan Cotterell Yoav Goldberg Shauli Ravfogel
Interventions targeting the representation space of language models (LMs)
have emerged as effective means to influence model behavior. These methods are
employed, for example, to eliminate or alter the encoding of demographic
information such as gender within the model's representations, creating a
counterfactual representation. However, since the intervention operates within
the representation space, understanding precisely which features it modifies
poses a challenge. We show that representation-space counterfactuals can be
converted into natural language counterfactuals. We demonstrate that this
approach enables us to analyze the linguistic alterations corresponding to a
given representation-space intervention and to interpret the features utilized
for encoding a specific concept. Moreover, the resulting counterfactuals can be
used to mitigate bias in classification.