# Data Imputation Based on Retrieval-Augmented Generation
## 1 Introduction  
With the rapid advancement of Big Data technologies, leveraging data to drive economic growth [1, 2], enhance government service levels [3], strengthen regulatory capabilities [4], and improve social governance [5] has become a prominent trend. Among the various types of data, relational data is widely used in practical applications, such as e-commerce recommendation systems [6, 7], medical diagnosis [8, 9, 10, 11], stock prediction [12], electricity consumption forecasting [13], and traffic prediction [14], among others. However, the promise of these applications of relational data is often hindered by incomplete or missing entries, which remain a critical bottleneck in realizing Big Data's full potential--a challenge that has persisted in database research for over three decades. Consequently, the task of data imputation, which aims to address missing or incomplete data values to establish a unified, high-quality data view, is of paramount importance. Nowadays, multi-source relational data may originate from sources such as the World Wide Web, various data lakes, and databases. These datasets frequently contain a significant number of missing values, exacerbating the problem of incomplete data. Therefore, applying effective imputation algorithms to enhance the quality of raw relational data is crucial for ensuring more accurate and reliable data analysis. However, existing data imputation methods struggle with missing, inconsistent, and low-quality inputs [15]; in this work, we addressthis gap by proposing a retrieval-augmented generation (RAG) framework tailored for robust data imputation. The importance and challenges of relational data imputation have been widely recognized by the international academic community. Over the past several decades, the field of relational data imputation has gone through several key development stages. In the early stages, research focused on utilizing statistical methods [16; 17; 18; 19; 20] to handle missing relational data. The earlier statistical imputation method involves using statistics (e.g., mean, median, or mode values) or the most similar among training data to replace missing components, such as mean imputation [17], hot deck imputation (HDJ) [20], cold deck imputation (CDI) [19], and K nearest neighbor imputation (KNNI) [18]. Subsequently, to address the challenges posed by the heterogeneity of multisource data, researchers introduced techniques such as deep learning pretraining [21; 22], concentrating on domain-level data imputation tasks. However, the unique structure of relational data makes its semantic information more difficult to capture and utilize. Specifically, different tables within the same databases or different columns within the same table may involve different domains [23; 24], leading to significant differences in embedded semantics. Currently, deep learning techniques still exhibit two main shortcomings in relational data imputation: (1) they struggles to effectively capture and process complex semantics in cross-domain relational data [25], resulting in reduced quality of data imputation [26]; (2) model training requires large-scale data, leading to expensive manual labeling costs (for instance, on Amazon's crowdsourcing platform, it costs approximately USD 0.1 to impute a table with missing values) [27]. In summary, traditional data imputation strategies face the primary issues of low quality and high costs. In recent years, large language models (LLMs) have undergone rapid advancements. Leveraging extensive parameter scales and complex model architectures, LLMs have acquired broad knowledge from vast Internet datasets [28], demonstrating strong capabilities in natural language understanding and reasoning. Moreover, compared to human labor, LLMs offer relatively low inference costs (for instance, the inference cost of GPT-4 is only USD 0.03 per thousand tokens).