Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing
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10726_2024_Article_9894.pdf
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1.93 MB
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Author(s)
Gimpel, Henner
Laubacher, Robert
Meindl, Oliver
Wöhl, Moritz
Dombetzki, Luca
Date Issued
July 30, 2024
Publisher
Springer Netherlands
Citation
Gimpel, H., Laubacher, R., Meindl, O. et al. Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing. Group Decis Negot
Version
Final published version
Abstract
Macro-task crowdsourcing presents a promising approach to address wicked problems like climate change by leveraging the collective efforts of a diverse crowd. Such macro-task crowdsourcing requires facilitation. However, in the facilitation process, traditionally aggregating and synthesizing text contributions from the crowd is labor-intensive, demanding expertise and time from facilitators. Recent advancements in large language models (LLMs) have demonstrated human-level performance in natural language processing. This paper proposes an abstract design for an information system, developed through four iterations of a prototype, to support the synthesis process of contributions using LLM-based natural language processing. The prototype demonstrated promising results, enhancing efficiency and effectiveness in synthesis activities for macro-task crowdsourcing facilitation. By streamlining the synthesis process, the proposed system significantly reduces the effort to synthesize content, allowing for stronger integration of synthesized content into the discussions to reach consensus, ideally leading to more meaningful outcomes.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1007/s10726-024-09894-w
