FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction
arXiv:2606.24679v1 Announce Type: cross Abstract: 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
The proliferation of complex and often messy real-world data is driving the need for more efficient and automated data preparation solutions, coinciding with advancements in LLM capabilities.
Automating data preparation, a notoriously time-consuming bottleneck in machine learning, significantly accelerates model development and deployment cycles across various industries.
Machine learning engineers and data scientists can leverage LLM-enhanced tools to construct robust data pipelines more rapidly and effectively, reducing manual effort and improving data quality.
- · Machine Learning Engineers
- · Data Scientists
- · Cloud AI Platforms
- · Enterprises deploying AI
- · Manual data cleaning/feature engineering consultancies
Increased efficiency in AI model development due to faster data preparation.
Expansion of AI applications into domains currently constrained by data quality and preparation burdens.
Further commoditization of foundational data science tasks, shifting human effort to higher-level model design and ethical considerations.
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Read at arXiv cs.AI