title: StyleRemix: Interpretable Authorship Obfuscation via Distillation and
    Perturbation of Style Elements
summary: 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
paper_authors._id: 66d10278c02f48f9078b51fb
paper_authors.name: Jillian Fisher
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-30T09:08:43.982Z
paper_authors._id: 6557a4b8a474e3962c9af99f
paper_authors.avatarUrl: /avatars/e6b35c2531ac60168f6f80259c2e5e6f.svg
paper_authors.fullname: Jillian Fisher
paper_authors.type: user
paper_authors.user: jrfish
paper_authors._id: 66d10278c02f48f9078b51fc
paper_authors.name: Skyler Hallinan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-30T07:18:08.784Z
paper_authors._id: 637d38fcb8e573d75bedc033
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637d38fcb8e573d75bedc033/MbzfbMBGN2ajH7oZtitcB.png
paper_authors.fullname: Skyler Hallinan
paper_authors.type: user
paper_authors.user: hallisky
paper_authors._id: 66d10278c02f48f9078b51fd
paper_authors.name: Ximing Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-30T09:08:49.785Z
paper_authors._id: 640928bd3461c51cf7378707
paper_authors.avatarUrl: /avatars/b29fcb7388b81f8686086352f6321d06.svg
paper_authors.fullname: Ximing Lu
paper_authors.type: user
paper_authors.user: Ximing
paper_authors._id: 66d10278c02f48f9078b51fe
paper_authors.name: Mitchell Gordon
paper_authors._id: 66d10278c02f48f9078b51ff
paper_authors.name: Zaid Harchaoui
paper_authors._id: 66d10278c02f48f9078b5200
paper_authors.name: Yejin Choi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-30T09:09:24.173Z
paper_authors._id: 64d42729f63b01b7f676b176
paper_authors.avatarUrl: /avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
paper_authors.fullname: Yejin Choi
paper_authors.type: user
paper_authors.user: yejinchoinka
paper_title: StyleRemix: Interpretable Authorship Obfuscation via Distillation and
    Perturbation of Style Elements
paper_summary: 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
paper_ai_summary: StyleRemix is an adaptive and interpretable authorship obfuscation method using pre-trained LoRA modules to perturb specific stylistic elements, outperforming state-of-the-art methods and larger LLMs.
paper_ai_keywords: StyleRemix Low Rank Adaptation (LoRA) stylistic elements authorship obfuscation