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Robust machine-generated text detection systems. Reveals that our attacks effectively compromise the performance of all testedĭetectors, thereby underscoring the urgent need for the development of more Leveraged in the attack can also be protected by detectors.
Instructing LLMs to generate synonymous word substitutions or writingĭirectives that modify the style without human involvement, and the LLMs In this paper, we systematically test the reliability of the existingĭetectors, by designing two types of attack strategies to fool the detectors:ġ) replacing words with their synonyms based on the context 2) altering the LLMs, recent works have proposed several algorithms to detect machine-generated To prevent the potentially deceptive usage of Significant safety and ethical risks when malicious users exploit them forĪutomated content generation. Download a PDF of the paper titled Red Teaming Language Model Detectors with Language Models, by Zhouxing Shi and 5 other authors Download PDF Abstract: The prevalence and high capacity of large language models (LLMs) present
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