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Authorship style transfer involves altering text to match the style of atarget author whilst preserving the original meaning. Existing unsupervisedapproaches like STRAP have largely focused on style transfer to target authorswith many examples of their writing style in books, speeches, or otherpublished works. This high-resource training data requirement (often greaterthan 100,000 words) makes these approaches primarily useful for style transferto published authors, politicians, or other well-known figures and authorshipstyles, while style transfer to non-famous authors has not been well-studied.We introduce the \textit{low-resource authorship style transfer} task, a morechallenging class of authorship style transfer where only a limited amount oftext in the target author's style may exist. In our experiments, wespecifically choose source and target authors from Reddit and style transfertheir Reddit posts, limiting ourselves to just 16 posts (on average ~500 words)of the target author's style. Style transfer accuracy is typically measured byhow often a classifier or human judge will classify an output as written by thetarget author. Recent authorship representations models excel at authorshipidentification even with just a few writing samples, making automaticevaluation of this task possible for the first time through evaluation metricswe propose. Our results establish an in-context learning technique we developas the strongest baseline, though we find current approaches do not yet achievemastery of this challenging task. We release our data and implementations toencourage further investigation.

Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?