# Data Imputation Based on Retrieval-Augmented Generation
## 2 Related Work  
### Data Imputation  
Missing data is a common issue in data analysis and machine learning tasks [36; 37]. During data collection, various factors can lead to missing data, such as respondents refusing to answer questions [38], privacy concerns [39], or technical limitations preventing the recording of information [40; 41]. Even a small amount of missing data can introduce significant biases and degrade the accuracy and quality of analysis [42; 43]. Consequently,research on missing data has received attention since the early stages of research in the field [44; 45], and various data imputation methods have been proposed. Based on the information sources utilized during data imputation, methods can be broadly categorized into internal imputation and external imputation [46]. Internal imputations rely on signals or redundancy within the dataset itself, inferring missing values solely from the existing data within the same dataset [47; 48]. While effective for self-contained datasets, these methods often lack generalizability to other datasets. In contrast, external imputations utilize user-specified external datasets [49; 50], which typically require carefully curated knowledge sources explicitly linked to the target data. However, these methods often depend on human intervention during the final stages of dataset imputation, limiting scalability and increasing costs. Based on different implementation approaches, data imputation methods can be categorized into four types. The simplest approach is to discard samples containing missing values [51]. However, this method introduces bias, compromises the representativeness of the dataset, exacerbates its incompleteness, and ultimately causes the final results to deviate from reality. Subsequently, a range of techniques emerge, spanning from simple statistical methods [17; 18; 19; 20] to traditional machine learning approaches [52; 53; 54; 55] and further to modern deep learning algorithms [21; 56; 57; 58; 59]. Early statistical methods, such as mean imputation [17], hot deck imputation (HDI) [20], cold deck imputation (CDI) [19], and K-nearest neighbors imputation (KNNI) [18], are computationally simple and widely used. However, they typically assume that data are MAR and are ineffective at handling MNAR scenarios. Additionally, they struggle to manage complex missing data patterns, which may lead to biased imputations and reduced analytical accuracy. Traditional machine learning approaches, such as XGBoost Imputation (XGBI) [60], MissForest Imputation (MissFI) [54], Multiple Imputation by Chained Equations (MICE) [61], Imputation via Individual Models (IIM) [55], Soft Imputation (SI) [62], Matrix Factorization Imputation (MFI) [63], Principal Component Analysis Imputation (PCAI) [64], Multilayer Perceptron Imputation (MLPI) [53], and Round-Robin Sinkhorn Imputation (RRSI) [52], have gained prominence due to their ability to effectively capture non-linear patterns. Furthermore, some methods, like MICE, offer the advantage of incorporating uncertainty through multiple imputations. Despite their advantages, these methods depend heavily on labeled data and require careful tuning of hyperparameters and assumptions about data structure. Modern deep learning algorithms leverage the power of generative models for imputation tasks. These methods either enhance the prior or posterior distributions of explicit generative models, such as deep autoencoders (AEs) [65; 66], or adopt alternative training objectives through implicit generative models like Generative Adversarial Networks (GANs) [67; 68]. While these methods excel in capturing complex patterns and are highly scalable for large-scale, high-dimensional datasets due to their hierarchical representation learning capabilities, they face several significant challenges: (1) Sensitivity to missing data patterns: The effectiveness of these models can be influenced by the distribution and extent of missing values.