Personalized Author Obfuscation with Large Language Models
This work addresses authorship privacy concerns for users by enhancing obfuscation techniques, though it is incremental as it builds on existing LLM methods.
The paper tackled the problem of using large language models (LLMs) for author obfuscation by analyzing user-wise performance, revealing a bimodal distribution in effectiveness, and proposed a personalized prompting method that improved performance and partially mitigated this issue.
In this paper, we investigate the efficacy of large language models (LLMs) in obfuscating authorship by paraphrasing and altering writing styles. Rather than adopting a holistic approach that evaluates performance across the entire dataset, we focus on user-wise performance to analyze how obfuscation effectiveness varies across individual authors. While LLMs are generally effective, we observe a bimodal distribution of efficacy, with performance varying significantly across users. To address this, we propose a personalized prompting method that outperforms standard prompting techniques and partially mitigates the bimodality issue.