A Survey on Task Assignment in Crowdsourcing
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Abstract (arXiv)
Quality improvement methods are essential to gathering high-quality crowdsourced data, both for research and industry applications. A popular and broadly applicable method is task assignment that dynamically adjusts crowd workflow parameters. In this survey, we review task assignment methods that address: heterogeneous task assignment, question assignment, and plurality problems in crowdsourcing. We discuss and contrast how these methods estimate worker performance, and highlight potential challenges in their implementation. Finally, we discuss future research directions for task assignment methods, and how crowdsourcing platforms and other stakeholders can benefit from them.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Nov 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 5.
8 papers.
- 2025Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absentcited by 15
- 2025What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patternscited by 4
- 2022Towards AI-Empowered Crowdsourcingcited by 0
- 2024Efficient Online Crowdsourcing with Complex Annotationscited by 0
- 2024A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deploymentcited by 0
- 2025DeCo: Defect-Aware Modeling with Contrasting Matching for Optimizing Task Assignment in Online IC Testingcited by 0
- 2025A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescuecited by 0
- 2025A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Taskscited by 0
- 2025A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks13 Oct 2025
- 2025A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue11 May 2025
- 2025DeCo: Defect-Aware Modeling with Contrasting Matching for Optimizing Task Assignment in Online IC Testing1 May 2025
- 2025What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patterns22 Apr 2025
- 2025Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent16 Feb 2025
- 2024A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment10 Mar 2024
- 2024Efficient Online Crowdsourcing with Complex Annotations25 Jan 2024
- 2022Towards AI-Empowered Crowdsourcing28 Dec 2022