Crowdsourcing has been increasingly utilized to gather subjective assessment, such as evaluating the toxicity of texts. Since there doesnot exist a single “ground truth” answer for subjective annotations, obtaining annotations to accurately reflect the opinions of differentsubgroups becomes a key objective for these subjective assessment tasks. Traditionally, this objective is accomplished by directly soliciting a large number of annotations from each subgroup, which can be costly especially when annotators of certain subgroups are hard to access. In this paper, using toxicity evaluation as an example, we explore the feasibility of using perspective taking—that is, asking annotators to take the point of views of a certain subgroup and estimate opinions within that subgroup—as a way to achieve this objective cost-efficiently. Our results show that compared to the baseline approach of directly soliciting annotations from the target subgroup, perspective taking could lead to better estimates of the subgroup-level opinion when annotations from the target subgroup is costly while the budget is limited. Moreover, prompting annotators to take the perspectives of contrasting subgroups simultaneously can further improve the quality of the estimates. Finally, we find that aggregating multiple perspective-taking annotations while soliciting a small number of annotations directly from the target subgroup for calibration leads to the highest-quality estimates under limited budget.

Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows, leading to the rise of a new paradigm of human-AI co-creation in writing. To understand how people perceive writings that are produced under this paradigm, in this paper, we conduct an experimental study to understand whether and how the disclosure of the level and type of AI assistance in the writing process would affect people’s perceptions of the writing on various aspects, including their evaluation on the quality of the writing, and their ranking of different writings. Our results suggest that disclosing the AI assistance in the writing process, especially if AI has provided assistance in generating new content, decreases the average quality ratings for both argumentative essays and creative stories. This decrease in the average quality ratings often comes with an increased level of variations in different individuals’ quality evaluations of the same writing. Indeed, factors such as an individual’s writing confidence and familiarity with AI writing assistants are shown to moderate the impact of AI assistance disclosure on their writing quality evaluations. We also find that disclosing the use of AI assistance may significantly reduce the proportion of writings produced with AI’s content generation assistance among the top-ranked writings.

The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment. Researchers have recently explored using large language models (LLMs) to generate synthetic datasets as an alternative approach. However, the effectiveness of the LLM-generated synthetic data in supporting model training is inconsistent across different classification tasks. To better understand factors that moderate the effectiveness of the LLM-generated synthetic data, in this study, we look into how the performance of models trained on these synthetic data may vary with the subjectivity of classification. Our results indicate that subjectivity, at both the task level and instance level, is negatively associated with the performance of the model trained on synthetic data. We conclude by discussing the implications of our work on the potential and limitations of leveraging LLM for synthetic data generation.