Gumbel Counterfactual Generation From Language Models

2024-11-11

AI Summary

This work addresses the interpretability and controllability of causal generation mechanisms in language models. We formally model language models as structural equation models (SEMs) and propose a counterfactual generation framework based on Gumbel-max reparameterization, rigorously distinguishing between “intervention” and “counterfactual” within Pearl’s causal hierarchy. Our approach enables joint modeling of original and counterfactual sentences conditioned on identical latent variable instances. By leveraging hindsight Gumbel sampling and latent noise inference, we generate string-level counterfactuals that are syntactically well-formed, semantically coherent, and causally faithful. Empirical evaluation reveals significant unintended side effects in prevailing representation-level intervention methods. Our contributions include: (i) a theoretically grounded SEM-based causal formulation of language generation; (ii) the first differentiable, string-level counterfactual generation framework with formal causal semantics; and (iii) an open, verifiable toolkit for probing and controlling causal behavior in language models.

Technology Category

• Machine Learning: Causal Learning
• Natural Language Processing: Generation
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social 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 emph{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 a structural equation model using the Gumbel-max trick, which we called Gumbel counterfactual generation. This reformulation 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.

Problem

• Understanding causal mechanisms in language models
• Generating true string counterfactuals using Gumbel-max trick
• Evaluating side effects of common intervention techniques

Innovation

• Gumbel-max trick for counterfactual generation
• Structural equation model reformulation
• Hindsight Gumbel sampling algorithm