Counterfactual Generation from Language Models
Authors: name: Shauli Ravfogel, name: Anej Svete, name: Vésteinn Snæbjarnarson, name: Ryan Cotterell

Understanding and manipulating the causal generation mechanisms in language
models is essential for controlling their behavior. Previous work has primarily
relied on techniques such as representation surgery -- e.g., model ablations or
manipulation of linear subspaces tied to specific concepts -- to intervene on
these models. To understand the impact of interventions precisely, it is useful
to examine counterfactuals -- e.g., how a given sentence would have appeared
had it been generated by the model following a specific intervention. We
highlight that counterfactual reasoning is conceptually distinct from
interventions, as articulated in Pearl's causal hierarchy. Based on this
observation, we propose a framework for generating true string counterfactuals
by reformulating language models as Generalized Structural-equation. Models
using the Gumbel-max trick. This allows us to model the joint distribution over
original strings and their counterfactuals resulting from the same
instantiation of the sampling noise. We develop an algorithm based on hindsight
Gumbel sampling that allows us to infer the latent noise variables and generate
counterfactuals of observed strings. Our experiments demonstrate that the
approach produces meaningful counterfactuals while at the same time showing
that commonly used intervention techniques have considerable undesired side
effects.