What Changed? Converting Representational Interventions to Natural Language
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.
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