  With the advent of larger and more complex deep learning models, such as in
Natural Language Processing (NLP), model qualities like explainability and
interpretability, albeit highly desirable, are becoming harder challenges to
tackle and solve. For example, state-of-the-art models in text classification
are black-box by design. Although standard explanation methods provide some
degree of explainability, these are mostly correlation-based methods and do not
provide much insight into the model. The alternative of causal explainability
is more desirable to achieve but extremely challenging in NLP due to a variety
of reasons. Inspired by recent endeavors to utilize Large Language Models
(LLMs) as experts, in this work, we aim to leverage the instruction-following
and textual understanding capabilities of recent state-of-the-art LLMs to
facilitate causal explainability via counterfactual explanation generation for
black-box text classifiers. To do this, we propose a three-step pipeline via
which, we use an off-the-shelf LLM to: (1) identify the latent or unobserved
features in the input text, (2) identify the input features associated with the
latent features, and finally (3) use the identified input features to generate
a counterfactual explanation. We experiment with our pipeline on multiple NLP
text classification datasets, with several recent LLMs, and present interesting
and promising findings.
