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通过仿真合成：通过可扩展的代理系统交互生成一致的企业数据Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
一种名为“通过仿真合成”（STS）的新方法被引入，利用大型语言模型（LLM）代理在仿真环境中生成一致的企业数据。这种无模式的数据合成方法不仅确保了结构有效性，还显著增强了分布保真度和可扩展性，相较于传统数据生成技术。通过利用代理系统交互，STS在数据合成领域代表了一项显著的进步，为企业应用提供了更可靠和有效的数据集。
A new approach called 'Synthesis Through Simulation' (STS) has been introduced, leveraging large language model (LLM) agents to generate coherent enterprise data within simulated environments. This schema-free data synthesis method not only ensures structural validity but also significantly enhances distributional fidelity and scalability compared to traditional data generation techniques. By utilizing agent-system interactions, STS represents a notable advancement in the field of data synthesis, promising more reliable and valid datasets for enterprise applications.
要点
通过仿真合成（STS）利用LLM代理进行数据生成。
Synthesis Through Simulation (STS) utilizes LLM agents for data generation.
该方法确保结构有效性并增强分布保真度。
The method ensures structural validity and enhances distributional fidelity.
STS在可扩展性方面优于传统数据合成技术。
STS outperforms traditional data synthesis techniques in scalability.
为何重要
数据合成的这一进展非常重要，因为它满足了企业环境中对高质量、可扩展数据集日益增长的需求，这对有效的决策和分析至关重要。
This advancement in data synthesis is significant as it addresses the growing need for high-quality, scalable datasets in enterprise environments, which are crucial for effective decision-making and analytics.
产品经理
对于产品经理而言，了解STS可以导致更好的数据驱动策略和改进的产品特性，因为它允许生成更准确和相关的数据集。
For product managers, understanding STS can lead to better data-driven strategies and improved product features, as it allows for the generation of more accurate and relevant datasets.
开发者
开发人员和工程师可以利用STS创建更高效的数据管道，提高用于机器学习模型的数据质量，从而增强整体系统性能。
Developers and engineers can leverage STS to create more efficient data pipelines and improve the quality of data used in machine learning models, enhancing overall system performance.
学生与求职者
对于学生和求职者来说，熟悉像STS这样的创新数据合成技术可以在就业市场中提供竞争优势，特别是在数据科学和人工智能领域。
For students and job-seekers, familiarity with innovative data synthesis techniques like STS can provide a competitive edge in the job market, particularly in data science and AI roles.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.AI
Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction'Synthesis Through Simulation' (STS) has been introduced as a new schema-free data synthesis approach that employs LLM agents to generate enterprise data in simulated environments, ensuring structural validity and outperforming traditional methods. This innovative technique enhances distributional fidelity and scalability in data generation.