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
66d10278c02f48f9078b51fb
Jillian Fisher
admin_assigned
2024-08-30T09:08:43.982Z
6557a4b8a474e3962c9af99f
/avatars/e6b35c2531ac60168f6f80259c2e5e6f.svg
Jillian Fisher
user
jrfish
66d10278c02f48f9078b51fc
Skyler Hallinan
claimed_verified
2024-08-30T07:18:08.784Z
637d38fcb8e573d75bedc033
https://cdn-avatars.huggingface.co/v1/production/uploads/637d38fcb8e573d75bedc033/MbzfbMBGN2ajH7oZtitcB.png
Skyler Hallinan
user
hallisky
66d10278c02f48f9078b51fd
Ximing Lu
admin_assigned
2024-08-30T09:08:49.785Z
640928bd3461c51cf7378707
/avatars/b29fcb7388b81f8686086352f6321d06.svg
Ximing Lu
user
Ximing
66d10278c02f48f9078b51fe
Mitchell Gordon
66d10278c02f48f9078b51ff
Zaid Harchaoui
66d10278c02f48f9078b5200
Yejin Choi
admin_assigned
2024-08-30T09:09:24.173Z
64d42729f63b01b7f676b176
/avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
Yejin Choi
user
yejinchoinka
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
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.
StyleRemix Low Rank Adaptation (LoRA) stylistic elements authorship obfuscation