Exploring the Cost-Effectiveness of Perspective Taking in Crowdsourcing Subjective Assessment: A Case Study of Toxicity Detection

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Abstract

Crowdsourcing has been increasingly utilized to gather subjective assessment, such as evaluating the toxicity of texts. Since there does not exist a single “ground truth” answer for subjective annotations, obtaining annotations to accurately reflect the opinions of different subgroups 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.

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APA

Duan, X., Li, Z., Ho, C. J., & Yin, M. (2025). Exploring the Cost-Effectiveness of Perspective Taking in Crowdsourcing Subjective Assessment: A Case Study of Toxicity Detection. In Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies: Long Papers, NAACL-HLT 2025 (Vol. 1, pp. 2359–2372). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2025.naacl-long.119

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