标题： 猫和老鼠——虚假文本生成能超越检测系统吗？ 显示英文标题

标题： Cat and Mouse -- Can Fake Text Generation Outpace Detector Systems?

摘要： 大型语言模型可以在学术写作、产品评论和政治新闻等领域生成有说服力的“虚假文本”。 已经研究了许多方法来检测人工生成的文本。 虽然这似乎预示着一场无休止的“军备竞赛”，但我们注意到，较新的大语言模型使用越来越多的参数、训练数据和能源，而相对简单的分类器则在资源有限的情况下表现出良好的检测准确性。 为了探讨模型击败检测器的能力是否会达到瓶颈，我们研究了统计分类器识别“虚假文本”的能力，这种文本风格类似于经典的侦探小说。 在0.5版本的提升中，我们发现Gemini在生成欺骗性文本方面的能力有所增强，而GPT则没有。 这表明，即使对于越来越大的模型，可靠地检测虚假文本可能仍然可行，尽管新的模型架构可能会提高其欺骗性。 显示英文摘要

摘要： Large language models can produce convincing "fake text" in domains such as academic writing, product reviews, and political news. Many approaches have been investigated for the detection of artificially generated text. While this may seem to presage an endless "arms race", we note that newer LLMs use ever more parameters, training data, and energy, while relatively simple classifiers demonstrate a good level of detection accuracy with modest resources. To approach the question of whether the models' ability to beat the detectors may therefore reach a plateau, we examine the ability of statistical classifiers to identify "fake text" in the style of classical detective fiction. Over a 0.5 version increase, we found that Gemini showed an increased ability to generate deceptive text, while GPT did not. This suggests that reliable detection of fake text may remain feasible even for ever-larger models, though new model architectures may improve their deceptiveness