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About this paper
Xin He, Junxi Shen, Yuchen Mou, David M. Bossens, Caishun Chen, Ivor W. Tsang, Yew Soon Ong
Researchers from A*STAR and Nanyang Technological University investigated how Large Language Model (LLM) agents can influence collective belief dynamics in online discussions. Their multi-agent simulations demonstrated that coordinated LLM agents can systematically induce and persist measurable shifts in the beliefs of human-like agent populations, with the extent of influence depending on topic consensus and intervention parameters.
Key insights
- Collective belief dynamics can transition from a stochastic social process to a systematic engineering problem due to the advanced capabilities of LLM agents.
- The effectiveness and pathways of programmable belief control are highly contextual, influenced by the target topic's initial consensus and the persuadability of uncommitted populations.
- Four structural properties—Indistinguishability, Persistence, Contextuality, and Configurability—make LLM agent-mediated influence inherently difficult to detect and defend against.
Summary by alphaXiv.
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