This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including annotations, preferences, instruction prompts, simulated dialogues, and free text. As these forms of LLM-generated data often intersect in their application, they exert mutual influence on each other and raise significant concerns about the quality and diversity of the artificial data incorporated into training cycles, leading to an artificial data ecosystem. To the best of our knowledge, this is the first study to aggregate various types of LLM-generated text data, from more tightly constrained data like "task labels" to more lightly constrained "free-form text". We then stress test the quality and implications of LLM-generated artificial data, comparing it with human data across various existing benchmarks. Despite artificial data's capability to match human performance, this paper reveals significant hidden disparities, especially in complex tasks where LLMs often miss the nuanced understanding of intrinsic human-generated content. This study critically examines diverse LLM-generated data and emphasizes the need for ethical practices in data creation and when using LLMs. It highlights the LLMs' shortcomings in replicating human traits and behaviors, underscoring the importance of addressing biases and artifacts produced in LLM-generated content for future research and development. All data and code are available on our project page.

本研究探讨了大型语言模型（LLMs）在生成人工数据中的不断扩大的作用。尽管人工数据能够与人类性能相匹配，但本文揭示了显著的潜在差异，尤其是在复杂任务中，LLMs常常错过对内在人类生成内容的微妙理解。该研究批判性地考察了多样化的LLM生成数据，并强调了在数据创建和使用LLMs时遵循道德实践的必要性。它凸显了解决LLM生成内容中产生的偏差和人为因素对于未来研究和开发的重要性。所有数据和代码都可在我们的项目页面上获得。

揭示LLM生成数据的本质