mlsanityData to 14 Oct 2026

Using LLMs to Advance the Cognitive Science of Collectives

arXiv 2506.00052 ↗First posted 28 May 2025q-bio.NC cs.AI cs.HCcited by 6

Not on the map. Why?

Abstract (arXiv)

LLMs are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to address the complexity that has hindered the study of collectives and raise possible risks that warrant new methods.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

12 of 17 references.

References · 12

  1. 2024LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individualscited by 249
  2. 2023Getting aligned on representational alignmentcited by 143
  3. 2023From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thoughtcited by 119
  4. 2024Large Language Models and Games: A Survey and Roadmapcited by 93
  5. 2024Building Machines that Learn and Think with Peoplecited by 73
  6. 2024Unintended Impacts of LLM Alignment on Global Representationcited by 66
  7. 2024Benchmarking Distributional Alignment of Large Language Modelscited by 61
  8. 2023Machine Culturecited by 55
  9. 2023Large language models predict human sensory judgments across six modalitiescited by 48
  10. 2024Centaur: a foundation model of human cognitioncited by 36
  11. 2021From partners to populations: A hierarchical Bayesian account of coordination and conventioncited by 27
  12. 2025On Benchmarking Human-Like Intelligence in Machinescited by 25

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.