Our work focuses on the challenge of detecting outputs generated by Large Language Models (LLMs) from those generated by humans. The ability to distinguish between the two is of utmost importance in numerous applications. However, the possibility and impossibility of such discernment have been subjects of debate within the community. Therefore, a central question is whether we can detect AI-generated text and, if so, when. In this work, we provide evidence that it should almost always be possible to detect the AI-generated text unless the distributions of human and machine generated texts are exactly the same over the entire support. This observation follows from the standard results in information theory and relies on the fact that if the machine text is becoming more like a human, we need more samples to detect it. We derive a precise sample complexity bound of AI-generated text detection, which tells how many samples are needed to detect. This gives rise to additional challenges of designing more complicated detectors that take in n samples to detect than just one, which is the scope of future research on this topic. Our empirical evaluations support our claim about the existence of better detectors demonstrating that AI-Generated text detection should be achievable in the majority of scenarios. Our results emphasize the importance of continued research in this area

翻译：本研究聚焦于区分大型语言模型生成内容与人类撰写内容的检测挑战。在众多应用场景中，区分二者的能力至关重要。然而，这种区分的可能性与局限性一直是学界争论的焦点。因此，核心问题在于：我们能否检测AI生成文本？若可检测，其条件为何？本研究表明，除非人类与机器生成文本的分布在完整支持集上完全一致，否则AI生成文本几乎总是可被检测。该结论源于信息论经典定理，其核心论据在于：若机器文本趋近人类水平，则需更多样本方能实现检测。我们推导出AI生成文本检测所需的精确样本复杂度，量化了检测所需样本数量。这一发现催生了新的研究挑战——相较于单样本检测，设计需要n个样本的更复杂检测器将成为该领域的未来研究方向。实证评估验证了更优检测器存在的论断，证明在多数场景下AI生成文本检测应可实现。研究结果凸显了持续探索该领域的重要性。