用自然语言定义数据操作，自动优化表格机器学习流程
SemPipes -- Optimizable Semantic Data Operators for Tabular Machine Learning Pipelines
- 用自然语言描述数据变换，由系统自动实现并优化
- 在多种任务上提升预测性能，同时简化流程复杂度
- 适合想减少手工编码的机器学习工程师和研究者
真实世界的表格机器学习依赖复杂的预处理流程，涵盖预测、数据整合、增强和调试。设计这些流程需要大量领域知识与工程投入，促使我们思考大语言模型（LLMs）如何通过代码生成支持表格机器学习。本文提出 SemPipes，一种将 LLM 驱动的语义数据操作符融入表格机器学习流水线的新颖声明式编程模型。语义操作符以自然语言指定数据转换，执行交由运行时系统完成。训练过程中，SemPipes 根据数据特征、操作指令和流水线上下文合成定制化的操作符实现。该设计使得通过基于进化搜索的 LLM 代码生成，实现流水线内数据操作的自动优化。我们在多样化的表格机器学习任务中评估 SemPipes，结果表明，语义操作符显著提升了专家设计及代理生成流水线的端到端预测性能，同时降低了流程复杂度。我们已在 Python 中实现 SemPipes，代码开源地址：https://github.com/deem-data/sempipes/tree/v1。
原文摘要 · Abstract (English)
Real-world machine learning on tabular data relies on complex data preparation pipelines for prediction, data integration, augmentation, and debugging. Designing these pipelines requires substantial domain expertise and engineering effort, motivating the question of how large language models (LLMs) can support tabular ML through code synthesis. We introduce SemPipes, a novel declarative programming model that integrates LLM-powered semantic data operators into tabular ML pipelines. Semantic operators specify data transformations in natural language while delegating execution to a runtime system. During training, SemPipes synthesizes custom operator implementations based on data characteristics, operator instructions, and pipeline context. This design enables the automatic optimization of data operations in a pipeline via LLM-based code synthesis guided by evolutionary search. We evaluate SemPipes across diverse tabular ML tasks and show that semantic operators substantially improve end-to-end predictive performance for both expert-designed and agent-generated pipelines, while reducing pipeline complexity. We implement SemPipes in Python and release it at https://github.com/deem-data/sempipes/tree/v1.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。