Feb. 15, 2024, 5:45 a.m. | Jillian Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang, Zaid Harchaoui, Yejin Choi

cs.CL updates on arXiv.org arxiv.org

arXiv:2402.08761v1 Announce Type: new
Abstract: The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship when needed, e.g., blind reviews for scientific papers, anonymous online reviews, or anonymous interactions in the mental health forums. In this paper, we propose an unsupervised inference-time approach to authorship obfuscation to address the unique challenges of authorship obfuscation: lack of supervision data for diverse authorship and domains, and …

abstract anonymous arxiv blind computational cs.ai cs.cl decoding health identification identity interactions language language models mental health online content papers privacy protect reviews small small language models type unsupervised

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