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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2606.24679v1

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Plain-Language Brief

Researchers propose a new method called FlowPipe to automatically construct data preparation pipelines for machine learning. The proposed method uses Conditional Generative Flow Networks (C-GFlowNets) and Deep Semantic Modulation to improve the efficiency of the pipeline construction process, particularly in dealing with sparse search spaces and invalid states. The new method outperforms existing state-of-the-art methods by an average of 11.96% in accuracy and achieves faster training convergence (12.5x).

What the Document Says

  • Data preparation pipelines are used to improve data quality for machine learning.
  • FlowPipe is a new 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.
  • Deep Semantic Modulation through Feature-wise Linear Modulation (FiLM) is used in FlowPipe, allowing LLM-derived logical priors to condition the policy's internal activations according to dataset semantics.
  • 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 existing state-of-the-art baselines, improving accuracy by an average of 11.96% and achieving faster training convergence (12.5x).
  • The source code for FlowPipe is available at https://github.com/KunyuNi/FlowPipe.

Key Concepts Mentioned

  • Data preparation pipelines
  • Machine learning
  • Conditional Generative Flow Networks (C-GFlowNets)
  • Deep Semantic Modulation through Feature-wise Linear Modulation (FiLM)
  • Failure awareness
  • Directed acyclic graph
  • Benchmark suites
  • State-of-the-art baselines

Claims Made by the Document

  • FlowPipe outperforms existing state-of-the-art methods.
  • FlowPipe improves accuracy by an average of 11.96%.

Evidence Used

  • Results from experiments on two benchmark suites with 74 real-world datasets.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.