# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 1 Introduction
In light of this, we introduce an approach that harnesses the similarities among respondents, the relevance among questions, and the semantic power of LLMs to create a more robust method for predicting missing data in surveys without the need for complex preprocessing or a lengthy training process. Our approach combines the statistical strengths of traditional imputation and tabular data classification methods with the advanced linguistic understanding provided by LLMs through prompt engineering. By integrating these three dimensions--respondent similarities, question relevance, and linguistic semantics--we enhance the overall accuracy and comprehensiveness of survey data analysis. This combined methodology is particularly effective in handling complex and nuanced scenarios, where both the interrelationships between features and rows as well as the linguistic context play crucial roles. Specifically, our approach generates the prompt to be input into the LLM in two main steps:  
* Row Selection: This involves selecting relevant respondents for the few-shot examples by calculating the cosine similarity between the responses of the current target user and those of other respondents. Respondents whose answers are most similar to the target user's responses are selected, ensuring that the few-shot examples include balanced options across all possible answer categories for the target question. * Column Selection: This step is used to include only the most relevant context for answering the target question. First, the LLM is prompted to generate a diverse set of questions designed to elicit responses pertinent to the target question. Subsequently,question-answer pairs corresponding to these generated questions are extracted from the existing dataset by the retriever. The LLM then uses the generated prompt, containing both the context from the target user and the selected few-shot examples, to predict the answer to the target question posed to the current respondent. A comprehensive overview of our approach is illustrated in Figure 1. Our contributions regarding our approach are summarized as follows:  
* Our approach consistently achieves high performance across various types of survey questions, effectively leveraging diverse information sources while maintaining robust results. * We propose a generalized LLM-based method for item non-response prediction that enables rapid inference without requiring complex preprocessing or additional training steps. * Our method is capable of handling scenarios where existing questions have very few respondents and can address new questions with no prior responses--capabilities that previous methods could not achieve. * Finally, our approach is scalable, maintaining effectiveness even as new respondents or data points are added in real-time. To evaluate the effectiveness of our LLM-based approach for predicting item non-responses, we present experimental results highlighting its performance relative to traditional methods, evaluated using the F1-score. Our model was compared with two imputation methods, including Multivariate Imputation by Chained Equations (MICE) [7], MissForest [8], as well as the deep learning model TabTransformer [13]. The results indicate that our model demonstrates competitive performance relative to these established methods. Furthermore, we conducted an ablation study to search for optimal hyperparameters, providing insights into the model's performance. Our approach shows that, due to its ability to operate without extensive preprocessing and training phases, it can deliver results more quickly than previous methods. Figure 1: An overall illustration of our method. Our method consists of two main steps for predicting item non-responses in survey data using an LLM: (1) Row Selection: This step involves identifying similar respondents to the target user based on their responses. Cosine similarity is calculated between the target user and other respondents. The most similar respondents are selected as few-shot examples to be included in the prompt.