StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements
<|reference_start|>StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements: 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<|reference_end|>
@article{fisher2024styleremix:,
    title={StyleRemix: Interpretable Authorship Obfuscation via Distillation and
  Perturbation of Style Elements},
    author={Jillian Fisher, Skyler Hallinan, Ximing Lu, Mitchell Gordon, Zaid
  Harchaoui, Yejin Choi},
    journal={arXiv preprint arXiv:2408.15666},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15666},
    primaryClass={cs.CL}
}