Title: Towards Prompt-Robust Machine-Generated Text Detection
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LLM detection, Rewrite-based detection, Learning distance, Prompt robust

Abstract: Modern large language models (LLMs) such as GPT, Claude, and Gemini have transformed the way we learn, work, and communicate. Yet, their ability to produce highly human-like text raises serious concerns about misinformation and academic integrity, making it an urgent need for reliable algorithms to detect LLM-generated content. In this paper, we start by presenting a geometric approach to demystify rewrite-based detection algorithms, revealing their underlying rationale and demonstrating their robustness in settings where prompts used to generate text are unobserved. Building on this insight, we introduce a novel rewrite-based detection algorithm that adaptively learns the distance between the original and rewritten text. We conduct extensive experiments with over 100 settings, and find that our approach demonstrates superior performance over baseline algorithms in the majority of scenarios. In particular, it achieves average improvements of 45.3% to 62.5% over the strongest baseline across different target LLMs (e.g., GPT, Claude, and Gemini), with gains reaching up to 100% in some cases.