# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 1 Introduction  
In many research fields, including economics [1], education [2], and healthcare [3], survey data are a critical resource for understanding human behavior and opinions. However, surveys often face the challenge of item non-response, where respondents skip certain questions, leading to incomplete data [4]. This not only compromises data integrity but also adversely impacts the accuracy of subsequent analyses. Particularly, when responses to critical key questions are missing, it can hinder precise analysis or even make analysis attempts impossible. Traditional approaches to address this issue have predominantly relied on statistical methods designed for imputation, such as mean imputation, KNN imputation [5], and multiple imputation [6; 7; 8]. These methods are well-suited for handling structured, tabular data where relationships between variables can be statistically modeled, offering robustness and consistency in maintaining the dataset's overall structure and trends. Additionally, to achieve better performance, these models combine statistical methods with machine learning techniques like Logistic Regression [9], Random Forest [10], and LightGBM [11]. As the complexity of data increased, deep learning models specifically designed for tabular data learning and classification, such as DeepGBM [12], TabTransformer [13], and SAINT [14], were introduced to leverage deep learning architectures for capturing complex patterns in tabular datasets. They represent a significant advancement by being able to model non-linear relationships and interactions between features that imputation methods might overlook. Among the many tabular dataset learning and classification methods, TabTransformer is particularly suitable for addressing the item non-response prediction problem as it is capable of handling missing values effectively compared to other models. However, previous imputation models inherently suffer from high time complexity and require manual selection of key features. In the context of deep learning models for tabular data classification, each feature needs to be learned individually, making these models less generalizable and more time-consuming as the model size increases. Particularly, both traditional imputation methods and advanced machine learning models require complex preprocessing steps, and the performance of the models is significantly influenced by the quality of the preprocessing. Furthermore, these models face significant limitations when applied to survey data, which often include rich linguistic content. Survey questions and responses are not merely numbers or simple categories; they encapsulate nuanced meanings, context, and subjective interpretations that purely imputation or tabular data classification approaches may fail to capture. As a result, there is a risk of losing valuable information, which could lead to suboptimal predictions. From the design phase of a survey to the subsequent analysis, the linguistic content plays a crucial role in shaping the insights drawn from the data [15]. Ignoring the semantic content means overlooking a wealth of information that could significantly enhance the accuracy of predictions and provide deeper analytical insights. With the advent of Large Language Models (LLMs), which possess remarkable in-context learning [16] and reasoning abilities [17] in the processing of natural language, a new opportunity has emerged. LLMs have the ability to reason using only the information provided in the prompt, without the need for additional training. This capability allows for the rapid prediction of item non-response, bypassing the difficulties and complexities of preprocessing and the need for extensive training processes. Moreover, LLMs possess the ability to understand the intricate nuances of language, including both contextual subtleties and underlying intents, thereby providing a means to incorporate semantic information directly into the prediction of item non-response problems.