用结构化提示工程让大模型自动完成数据科学任务
CEDAR: Context Engineering for Agentic Data Science
- 用分步计划+代码块结构化提示，提升任务可读性
- 在Kaggle经典挑战中实现端到端自动化，保持数据本地化
- 适合希望自动化数据处理流程的开发者和研究者
我们提出CEDAR，一种基于代理架构自动化数据科学任务的应用。利用大语言模型解决数据科学问题仍处于探索阶段，但具有巨大市场潜力。主要挑战包括任务复杂度高、数据量大、计算资源有限以及上下文长度受限。我们证明通过有效的上下文工程可缓解这些问题。首先，在初始提示中引入数据科学专用输入字段，为代理系统提供明确指令。解决方案以枚举形式的交替计划与代码块序列生成，由不同LLM代理协作完成，确保工作流任意步骤的上下文清晰可读。通过函数调用生成中间文本和对应Python代码，保证数据本地存储，仅将聚合统计信息及指令注入提示。通过迭代式代码生成与智能历史渲染，实现容错与上下文管理。在Kaggle典型挑战中验证了该代理数据科学家的有效性。
原文摘要 · Abstract (English)
We demonstrate CEDAR, an application for automating data science (DS) tasks with an agentic setup. Solving DS problems with LLMs is an underexplored area that has immense market value. The challenges are manifold: task complexities, data sizes, computational limitations, and context restrictions. We show that these can be alleviated via effective context engineering. We first impose structure into the initial prompt with DS-specific input fields, that serve as instructions for the agentic system. The solution is then materialized as an enumerated sequence of interleaved plan and code blocks generated by separate LLM agents, providing a readable structure to the context at any step of the workflow. Function calls for generating these intermediate texts, and for corresponding Python code, ensure that data stays local, and only aggregate statistics and associated instructions are injected into LLM prompts. Fault tolerance and context management are introduced via iterative code generation and smart history rendering. The viability of our agentic data scientist is demonstrated using canonical Kaggle challenges.
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