FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction

FlowPipe introduces a novel framework for constructing data preparation pipelines using Conditional Generative Flow Networks (C-GFlowNets) and a Trajectory Balance objective to improve long-horizon credit assignment and exploration efficiency. By integrating Deep Semantic Modulation via Feature-wise Linear Modulation (FiLM), it allows for better conditioning of the pipeline decisions based on dataset semantics. Evaluated on 74 real-world datasets, FlowPipe demonstrates an average accuracy improvement of 11.96% and a 12.5x increase in training convergence speed compared to state-of-the-art methods, making it a significant advancement for practitioners in automated data pipeline construction.