Data Imputation Based on Retrieval-Augmented Generation

Modern organizations collect increasing volumes of data to drive decision-making, often stored in centralized repositories such as data lakes, which consist of diverse structured and unstructured datasets. However, these repositories often suffer from issues such as incomplete, inconsistent, and low-quality data, which hinder data-driven insights. Existing methods for data imputation, including statistical techniques and machine learning approaches, often rely heavily on large amounts of labeled data and domain-specific knowledge, making them labor-intensive and limited in handling semantic heterogeneity across data formats. To address these challenges, this study proposes a novel retrieval-augmented generation (RAG) framework for data imputation that effectively combines the strengths of retrieval mechanisms and large language models (LLMs). This approach constructs semantic-based indexes for heterogeneous data, employs a recall and re-ranking strategy to enhance retrieval relevance, and proposes a method to reduce inference cost while ensuring imputation quality. Extensive experiments on real-world datasets demonstrate that the proposed framework significantly outperforms machine learning and deep learning approaches.