# Data Imputation Based on Retrieval-Augmented Generation
## 2 Related Work
### Data Imputation
(2) High-quality training data requirements: These models require large, diverse datasets to achieve optimal performance. (3) Computational complexity: Both GANs and AEs demand substantial computational resources. With their rapid development, the use of LLMs for data imputation has garnered significant attention from researchers [29; 69; 70; 71]. Prompt engineering enables the application of LLMs to specific downstream tasks, improving their accuracy without modifying model parameters [72]. This method leverages the contextual learning capabilities of LLMs by constructing inputs that incorporate additional prompts, such as task instructions or labeled examples, into the task descriptions. Studies have shown that prompt quality critically affects LLM performance in data imputation [73]. In zero-shot learning--where only the task description is provided--no labeled examples are needed, but results often lag due to missing task-specific context. In contrast, few-shot learning supplies a small set of labeled examples, enabling LLMs to better capture imputation patterns and generally outperform zero-shot methods [74; 75]. However, its effectiveness varies widely with the choice of examples, as different example combinations can lead to large swings in accuracy. Subsequent research has explored training methods to enable LLMs to better understand the structure and knowledge of tabular data, thereby improving their performance in data imputation [73]. However, as the knowledge in these models is stored within their parameters, even with enhanced training, there is no guarantee that the imputed values will achieve consistently high levels of accuracy and reliability [69]. A promising approach to address this limitation is the adoption of retrieval-augmented methods [76]. Among these, RATA [77] leverages a retrieval-augmented transformer model for table enhancement tasks, including data imputation. However, RATA primarily focuses on table-level retrieval, such as considering the number of rows and columns, without leveraging specific external knowledge to estimate missing values more accurately within the tabular data. While training methods and retrieval-augmented approaches have shown potential in improving LLMs' capabilities for data imputation, these methods have certain limitations [78; 79]. The reliance on model parameters or table-level retrieval alone often falls short when addressing tasks that require deeper contextual understanding or integration of external knowledge [80]. Building upon advancements in training methods and retrieval-augmented techniques, some studies have taken an alternative approach by leveraging prompt engineering techniques. By decomposing complex tasks into smaller steps and utilizing multi-turn dialogues [81; 82], these methods enable LLMs to process information in a structured, chain-like, or tree-like manner [34]. While prompt engineering enables LLMs to address data imputation tasks with relatively simple input construction, it inherently limits the amount of task-related information the model can access. Consequently, for highly specialized or complex tasks, prompt engineering alone may fail to deliver satisfactory results. Previous studies have shown that applying LLMs to data imputation tasks is a flexible and efficient approach. However, the limitations of LLMs in complex and domain-specific tasks have prompted the exploration of complementary strategies to fully unleash their potential.