Counterfactual Generation from Language Models
Shauli Ravfogel, Anej Svete, Vésteinn Snæbjarnarson, Ryan Cotterell
2024-11-12
Summary
This paper discusses how to generate counterfactuals, which are alternative versions of text that show what could have happened if different choices were made, using language models.
What's the problem?
Understanding how language models generate text is important for controlling their behavior, especially when trying to correct errors or explore different outcomes. Previous methods for improving these models often relied on techniques that manipulated specific parts of the model, but they didn't fully explain how these changes affected the generated text. This makes it hard to know how to effectively intervene in the model's output.
What's the solution?
The authors propose a new framework for generating true counterfactuals by reformulating language models using a method called Generalized Structural-equation Models combined with the Gumbel-max trick. This approach allows them to create alternative versions of sentences based on specific interventions. They developed an algorithm that helps infer hidden variables in the model, leading to meaningful counterfactuals. Their experiments show that this method produces useful counterfactuals while highlighting that traditional intervention techniques can have unwanted side effects.
Why it matters?
This research is important because it enhances our understanding of how language models generate text and how we can manipulate that process to explore different scenarios. By generating counterfactuals, we can better evaluate and improve the performance of these models, making them more reliable for applications in areas like natural language processing, AI development, and decision-making systems.
Abstract
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.