Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing

Henner Gimpel, Robert Laubacher, Oliver Meindl (), Moritz Wöhl and Luca Dombetzki
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Henner Gimpel: FIM Research Center for Information Management
Robert Laubacher: Massachusetts Institute of Technology (MIT)
Oliver Meindl: FIM Research Center for Information Management
Moritz Wöhl: FIM Research Center for Information Management
Luca Dombetzki: TUM.ai

Group Decision and Negotiation, 2024, vol. 33, issue 5, No 13, 1322 pages

Abstract: 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.

Keywords: Action design research; Facilitation; Large language model; Macro-task crowdsourcing; Natural language processing; Synthesis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10726-024-09894-w

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