This paper explores the artifactuality of large language model (LLM)-generated data, highlighting the challenges and biases inherent in artificial data produced by LLMs. The study aggregates various types of LLM-generated text data, ranging from tightly constrained "task labels" to more open-ended "free-form text," and evaluates their quality and implications across different benchmarks. The research reveals significant disparities between LLM-generated data and human data, particularly in complex tasks where LLMs often fail to capture the nuanced understanding of human-generated content. The study emphasizes the need for ethical practices in data creation and the use of LLMs, as well as the importance of addressing biases and artifacts in LLM-generated content for future research and development. The paper investigates five types of LLM-generated data: task labels, preferences, instructions, simulations, and free-form text. Each type is analyzed for its characteristics, biases, and potential impacts on downstream tasks. The study finds that LLMs often over-represent majority opinions and fail to capture minority perspectives, leading to biased and inconsistent outputs. Additionally, LLMs struggle with unfamiliar scenarios, generating incorrect outputs and exhibiting biases that can affect the performance of models trained on LLM-generated data. The research also examines the effects of using LLM-generated data in training models, finding that it can lead to degraded performance and amplified biases. The study highlights the importance of monitoring and addressing these issues to ensure that LLM-generated data is used ethically and responsibly. The paper concludes that while LLMs can match human performance in some tasks, they often lack the nuanced understanding and diversity of human-generated data, underscoring the need for careful and responsible methods in data creation and model development. The study provides a comprehensive analysis of the challenges and implications of LLM-generated data, offering insights into the need for further research and ethical considerations in the use of artificial data.This paper explores the artifactuality of large language model (LLM)-generated data, highlighting the challenges and biases inherent in artificial data produced by LLMs. The study aggregates various types of LLM-generated text data, ranging from tightly constrained "task labels" to more open-ended "free-form text," and evaluates their quality and implications across different benchmarks. The research reveals significant disparities between LLM-generated data and human data, particularly in complex tasks where LLMs often fail to capture the nuanced understanding of human-generated content. The study emphasizes the need for ethical practices in data creation and the use of LLMs, as well as the importance of addressing biases and artifacts in LLM-generated content for future research and development. The paper investigates five types of LLM-generated data: task labels, preferences, instructions, simulations, and free-form text. Each type is analyzed for its characteristics, biases, and potential impacts on downstream tasks. The study finds that LLMs often over-represent majority opinions and fail to capture minority perspectives, leading to biased and inconsistent outputs. Additionally, LLMs struggle with unfamiliar scenarios, generating incorrect outputs and exhibiting biases that can affect the performance of models trained on LLM-generated data. The research also examines the effects of using LLM-generated data in training models, finding that it can lead to degraded performance and amplified biases. The study highlights the importance of monitoring and addressing these issues to ensure that LLM-generated data is used ethically and responsibly. The paper concludes that while LLMs can match human performance in some tasks, they often lack the nuanced understanding and diversity of human-generated data, underscoring the need for careful and responsible methods in data creation and model development. The study provides a comprehensive analysis of the challenges and implications of LLM-generated data, offering insights into the need for further research and ethical considerations in the use of artificial data.