# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 4 Experiments
### Baselines  
1. MICE: Multivariate imputation by chained equations (MICE) [7] is the most common method for imputing missing data. In the MICE algorithm, imputation can be performed using a variety of parametric and nonparametric methods. For our study, we configured MICE to use a non-parametric method, with the number of datasets set to 2 and the number of iterations also set to 2.
2. Missforest: MissForest [8] is an iterative imputation method that utilizes random forests to predict and fill in missing values within a dataset, capable of handling different types of variables, such as categorical and continuous variables. For our implementation, we configured the random forest using the default settings from the sklearn library [35] and performed 5 iterations.
3. TabTransformer: TabTransformer [13] is a tabular data modeling architecture that adapts Transformer layers to the tabular domain by applying multi-head attention on parametric embeddings, effectively managing missing values. For our approach, we set all hyperparameters to their default values as referenced in the original paper, with the exception of the epochs, learning rate, and batch size, which were set to 10, 0.0001, and 1, respectively. To handle missing values, we replaced all missing values with 0.
4. Naive Prompt: In addition to evaluating sophisticated methods, we also implemented a Naive Prompt approach as a baseline. This method is inspired by the Naive RAG [31] approach, where the selection of relevant questions is simplified to assess the impact of more advanced techniques. In the Naive Prompt approach, we performed column selection without leveraging any advanced techniques to rewrite or refine the selected questions. Specifically, during the column selection process, instead of asking the LLM to rewrite relevant questions, we simply selected the questions based on their cosine similarity between the target question and the user's non-target question-response pairs. These selected questions were then added to the context in descending order of their cosine similarity scores.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Code** & **Question** & **Answers** \\ \hline SATIS & All in all, are you satisfied or dissatisfied with the way things are going in this country today? & Satisfied \\ \hline POL10 & Do you think about your vote for Congress & A vote FOR [PERSON] \\  & this fall as... & A vote AGAINST [PERSON] \\ \hline POL1 JB & Do you approve or disapprove of the way & Approve \\  & [PERSON] is handling his job as president? & Disapprove \\ \hline DRLEAD & In your view, which party has better political leaders? & The Republican Party \\ \hline PERSFNCB & A year from now, do you expect that the & Better \\  & financial situation of you and your family & Worse \\  & will be... & About the same as now \\ \hline ECON1B & A year from now, do you expect that economic conditions in the country as a whole & Better \\  & will be... & Worse \\  & will be... & About the same as now \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of survey questions used in the experiment, including the question codes, the full text of each question, and the corresponding answer options. Each question targets a specific aspect of respondents’ opinions on political and economic topics, with multiple-choice answers reflecting the possible range of responses.