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Large language models (LLMs) are changing how information is shared online, impacting collective intelligence. Further research is needed to understand LLMs’ effects on group problem-solving capabilities.

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Area of Science:

  • Social Sciences
  • Computer Science
  • Information Science

Background:

  • Collective intelligence enables groups to outperform individuals through distributed cognition and coordination.
  • Information technology has historically supported collective intelligence via prediction markets, forums, and crowdsourcing platforms.
  • Large language models (LLMs) represent a significant technological shift in online information processing.

Purpose of the Study:

  • To examine the opportunities and challenges presented by large language models for collective intelligence.
  • To synthesize interdisciplinary perspectives on the impact of LLMs on group problem-solving.
  • To identify potential benefits, risks, and policy considerations related to LLMs and collective intelligence.

Main Methods:

  • Interdisciplinary review synthesizing perspectives from industry and academia.
  • Analysis of how LLMs transform information aggregation, access, and transmission.
  • Identification of research questions and policy-relevant considerations.

    • Main Results:

    • LLMs present unique opportunities and challenges for collective intelligence.
    • The transformation of online information dynamics by LLMs requires careful consideration.
    • Potential benefits and risks associated with LLMs in collective settings are highlighted.

    Conclusions:

    • A closer examination of how LLMs affect humans' ability to collectively tackle complex problems is crucial.
    • Understanding the interplay between LLMs and collective intelligence is essential for future societal and organizational success.
    • Further research is needed to navigate the evolving landscape of AI-enhanced collective intelligence.