LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction

• URL: http://arxiv.org/abs/2507.13712v1
• Date: Fri, 18 Jul 2025 07:52:19 GMT
• Title: LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
• Authors: Jing Chang, Chang Liu, Jinbin Huang, Rui Mao, Jianbin Qin,
• Abstract summary: We present LLaPipe, a novel framework that integrates Large Language Models (LLMs) as intelligent policy advisors.<n>We demonstrate that LLaPipe achieves up to 22.4% improvement in pipeline quality and 2.3$times$ faster convergence compared to state-of-the-art RL-based methods.
• Score: 10.777588304625718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated data preparation is crucial for democratizing machine learning, yet existing reinforcement learning (RL) based approaches suffer from inefficient exploration in the vast space of possible preprocessing pipelines. We present LLaPipe, a novel framework that addresses this exploration bottleneck by integrating Large Language Models (LLMs) as intelligent policy advisors. Unlike traditional methods that rely solely on statistical features and blind trial-and-error, LLaPipe leverages the semantic understanding capabilities of LLMs to provide contextually relevant exploration guidance. Our framework introduces three key innovations: (1) an LLM Policy Advisor that analyzes dataset semantics and pipeline history to suggest promising preprocessing operations, (2) an Experience Distillation mechanism that mines successful patterns from past pipelines and transfers this knowledge to guide future exploration, and (3) an Adaptive Advisor Triggering strategy (Advisor\textsuperscript{+}) that dynamically determines when LLM intervention is most beneficial, balancing exploration effectiveness with computational cost. Through extensive experiments on 18 diverse datasets spanning multiple domains, we demonstrate that LLaPipe achieves up to 22.4\% improvement in pipeline quality and 2.3$\times$ faster convergence compared to state-of-the-art RL-based methods, while maintaining computational efficiency through selective LLM usage (averaging only 19.0\% of total exploration steps).

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