Causal explanations of the predictions of NLP systems are essential to ensure
safety and establish trust. Yet, existing methods often fall short of
explaining model predictions effectively or efficiently and are often
model-specific. In this paper, we address model-agnostic explanations,
proposing two approaches for counterfactual (CF) approximation. The first
approach is CF generation, where a large language model (LLM) is prompted to
change a specific text concept while keeping confounding concepts unchanged.
While this approach is demonstrated to be very effective, applying LLM at
inference-time is costly. We hence present a second approach based on matching,
and propose a method that is guided by an LLM at training-time and learns a
dedicated embedding space. This space is faithful to a given causal graph and
effectively serves to identify matches that approximate CFs. After showing
theoretically that approximating CFs is required in order to construct faithful
explanations, we benchmark our approaches and explain several models, including
LLMs with billions of parameters. Our empirical results demonstrate the
excellent performance of CF generation models as model-agnostic explainers.
Moreover, our matching approach, which requires far less test-time resources,
also provides effective explanations, surpassing many baselines. We also find
that Top-K techniques universally improve every tested method. Finally, we
showcase the potential of LLMs in constructing new benchmarks for model
explanation and subsequently validate our conclusions. Our work illuminates new
pathways for efficient and accurate approaches to interpreting NLP systems.

解释自然语言处理系统预测的因果解释对于确保安全性和建立信任至关重要，本文提出了两种针对模型无关性的倒因果估算方法，分别基于生成和匹配，并通过实验证明了生成模型和匹配模型在模型解释方面的出色性能。