自动搭数据管道的难题，FlowPipe用LLM+生成流网络解决了，74个数据集上准确率升12%，训练快12倍多，比老方法强一截。
论文提出FlowPipe，将数据预处理管道构建转化为有向无环图上的条件概率流生成问题。FlowPipe基于条件生成流网络（C-GFlowNets）与轨迹平衡目标，将终端验证奖励与早期决策联系。通过深度语义调制（FiLM）注入LLM提取的逻辑先验，并加入失败感知机制避免无效状态。在包含74个真实数据集的基准测试中，FlowPipe比Multi-DQN基线平均准确率提升11.96%，训练收敛速度提升12.5倍。
FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction
Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators. However, automatically constructing such pipelines is computationally difficult because operator sequences are combinatorial and end-to-end evaluation is expensive. Existing state-of-the-art (SOTA) Multi-DQN methods still face three key limitations: decoupled value estimators weaken long-horizon credit assignment, dataset context is only weakly injected into the policy, and exploration is inefficient in a sparse search space with many invalid states. To address these issues, we propose FlowPipe, a unified framework that formulates pipeline synthesis as conditional probabilistic flow generation over a directed acyclic graph. FlowPipe uses Conditional Generative Flow Networks (C-GFlowNets) with a Trajectory Balance objective to connect terminal validation rewards with early pipeline decisions. It further introduces Deep Semantic Modulation through Feature-wise Linear Modulation (FiLM), allowing LLM-derived logical priors to condition the policy's internal activations according to dataset semantics. In addition, FlowPipe incorporates failure awareness into the flow objective to avoid invalid states and concentrate search on high-potential regions. Experiments on two benchmark suites with 74 real-world datasets show that FlowPipe outperforms SOTA baselines, improving accuracy by 11.96% on average and achieving 12.5x faster training convergence. Source code is available at https://github.com/KunyuNi/FlowPipe.