  The counterfactual token generation has been limited to perturbing only a
single token in texts that are generally short and single sentences. These
tokens are often associated with one of many sensitive attributes. With limited
counterfactuals generated, the goal to achieve invariant nature for machine
learning classification models towards any sensitive attribute gets bounded,
and the formulation of Counterfactual Fairness gets narrowed. In this paper, we
overcome these limitations by solving root problems and opening bigger domains
for understanding. We have curated a resource of sensitive tokens and their
corresponding perturbation tokens, even extending the support beyond
traditionally used sensitive attributes like Age, Gender, Race to Nationality,
Disability, and Religion. The concept of Counterfactual Generation has been
extended to multi-token support valid over all forms of texts and documents. We
define the method of generating counterfactuals by perturbing multiple
sensitive tokens as Counterfactual Multi-token Generation. The method has been
conceptualized to showcase significant performance improvement over
single-token methods and validated over multiple benchmark datasets. The
emendation in counterfactual generation propagates in achieving improved
Counterfactual Multi-token Fairness.
