  Annotating data via crowdsourcing is time-consuming and expensive. Due to
these costs, dataset creators often have each annotator label only a small
subset of the data. This leads to sparse datasets with examples that are marked
by few annotators. The downside of this process is that if an annotator doesn't
get to label a particular example, their perspective on it is missed. This is
especially concerning for subjective NLP datasets where there is no single
correct label: people may have different valid opinions. Thus, we propose using
imputation methods to generate the opinions of all annotators for all examples,
creating a dataset that does not leave out any annotator's view. We then train
and prompt models, using data from the imputed dataset, to make predictions
about the distribution of responses and individual annotations.
  In our analysis of the results, we found that the choice of imputation method
significantly impacts soft label changes and distribution. While the imputation
introduces noise in the prediction of the original dataset, it has shown
potential in enhancing shots for prompts, particularly for low-response-rate
annotators. We have made all of our code and data publicly available.
