FlowPipe is a framework that models data preprocessing pipeline construction as conditional probability flow generation on directed acyclic graphs, leveraging conditional generative flow networks to tackle combinatorial optimization.

Why does it improve machine learning outcomes?

By integrating a deep semantic modulation mechanism using large language models, FlowPipe dynamically adapts internal activations to dataset characteristics, generating preprocessing sequences tailored to specific data distributions.

What should the community watch for next?

With its code now open-sourced, future research will likely explore advanced LLM integration strategies and extensions to other pipeline synthesis tasks, potentially democratizing automated data preparation.

FlowPipe：利用大語言模型增強條件生成流網絡構建數據預處理流水線

機器學習中的數據預處理流水線構建面臨組合爆炸與端到端評估昂貴的挑戰。現有基於強化學習的方法存在信用分配弱、上下文注入不足及探索效率低等局限。本文提出FlowPipe框架，將流水線合成建模為有向無環圖上的條件概率流生成問題。該方法採用條件生成流網絡（C-GFlowNets）結合軌跡平衡目標，實現從早期決策到最終驗證獎勵的有效連接。通過引入基於大語言模型語義的深度語義調製（FiLM），策略網絡能根據數據集特徵動態調整內部激活。此外，FlowPipe在流目標中融入失敗感知機制，有效規避無效狀態。在74個真實數據集的基準測試中，FlowPipe平均提升準確率11.96%，訓練收斂速度加快12.5倍，顯著優於現有最先進方法。

Sources

arXiv