Large Language Models (LLM) are already widely used to generate content for a variety of online platforms. As we are not able to safely distinguish LLM-generated content from human-produced content, LLM-generated content is used to train the next generation of LLMs, giving rise to a self-consuming training loop. From the image generation domain we know that such a self-consuming training loop reduces both quality and diversity of images finally ending in a model collapse. However, it is unclear whether this alarming effect can also be observed for LLMs. Therefore, we present the first study investigating the self-consuming training loop for LLMs. Further, we propose a novel method based on logic expressions that allows us to unambiguously verify the correctness of LLM-generated content, which is difficult for natural language text. We find that the self-consuming training loop produces correct outputs, however, the output declines in its diversity depending on the proportion of the used generated data. Fresh data can slow down this decline, but not stop it. Given these concerning results, we encourage researchers to study methods to negate this process.

翻译：大语言模型（LLM）已被广泛用于为各类在线平台生成内容。由于我们无法安全地区分LLM生成内容与人类创作内容，LLM生成内容被用于训练下一代LLM，从而形成了自消耗训练循环。从图像生成领域可知，此类自消耗训练循环会降低图像的质量与多样性，最终导致模型崩溃。然而，这种警示性效应是否同样存在于LLM中尚不明确。为此，我们首次开展了针对LLM自消耗训练循环的研究。进一步地，我们提出了一种基于逻辑表达式的新方法，该方法能够明确验证LLM生成内容的正确性——这对自然语言文本而言通常较为困难。研究发现，自消耗训练循环虽能产生正确输出，但其输出的多样性会随着所用生成数据比例的增加而下降。新鲜数据可延缓这种衰退，却无法阻止其发生。鉴于这些令人担忧的结果，我们呼吁研究者积极探索阻断该过程的方法。