StyleRemix: Interpretable Authorship Obfuscation via Distillation and
  Perturbation of Style Elements
Authors: name: Jillian Fisher, name: Skyler Hallinan, name: Ximing Lu, name: Mitchell Gordon, name: Zaid Harchaoui, name: Yejin Choi

Authorship obfuscation, rewriting a text to intentionally obscure the
identity of the author, is an important but challenging task. Current methods
using large language models (LLMs) lack interpretability and controllability,
often ignoring author-specific stylistic features, resulting in less robust
performance overall.
  To address this, we develop StyleRemix, an adaptive and interpretable
obfuscation method that perturbs specific, fine-grained style elements of the
original input text. StyleRemix uses pre-trained Low Rank Adaptation (LoRA)
modules to rewrite an input specifically along various stylistic axes (e.g.,
formality and length) while maintaining low computational cost. StyleRemix
outperforms state-of-the-art baselines and much larger LLMs in a variety of
domains as assessed by both automatic and human evaluation.
  Additionally, we release AuthorMix, a large set of 30K high-quality,
long-form texts from a diverse set of 14 authors and 4 domains, and DiSC, a
parallel corpus of 1,500 texts spanning seven style axes in 16 unique
directions