  Natural Language Counterfactual generation aims to minimally modify a given
text such that the modified text will be classified into a different class. The
generated counterfactuals provide insight into the reasoning behind a model's
predictions by highlighting which words significantly influence the outcomes.
Additionally, they can be used to detect model fairness issues or augment the
training data to enhance the model's robustness. A substantial amount of
research has been conducted to generate counterfactuals for various NLP tasks,
employing different models and methodologies. With the rapid growth of studies
in this field, a systematic review is crucial to guide future researchers and
developers. To bridge this gap, this survey comprehensively overview textual
counterfactual generation methods, particularly including those based on Large
Language Models. We propose a new taxonomy that categorizes the generation
methods into four groups and systematically summarize the metrics for
evaluating the generation quality. Finally, we discuss ongoing research
challenges and outline promising directions for future work.
