A Bayesian framework disentangles topic, agreement, and anchoring biases from interaction effects in LLM multi-turn dialogues, revealing convergence to attractors that shift with fine-tuning.
Testing theory of mind in large language models and humans.Nature Human Behaviour, 8(7):1285–1295, 2024
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LLM agents in controlled network debates show agreement drift toward specific opinion positions, requiring separation of structural effects from LLM biases before using them as human behavioral proxies.
Observational analysis of Brazilian YouTube climate content identifies psychological engagement traits and explores their use in generative AI campaigns, accompanied by a public dataset of 226K videos and 2.7M comments.
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Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models
A Bayesian framework disentangles topic, agreement, and anchoring biases from interaction effects in LLM multi-turn dialogues, revealing convergence to attractors that shift with fine-tuning.
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Network Effects and Agreement Drift in LLM Debates
LLM agents in controlled network debates show agreement drift toward specific opinion positions, requiring separation of structural effects from LLM biases before using them as human behavioral proxies.
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Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse
Observational analysis of Brazilian YouTube climate content identifies psychological engagement traits and explores their use in generative AI campaigns, accompanied by a public dataset of 226K videos and 2.7M comments.