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A theory of Bayesian intelligence for agents such as language models is developed and the difficulty of aggregating coarse reports from intelligent agents is shown: unless the agent reports a belief about the complete state of the world, the optimal aggregation can assign arbitrary weights to states that have not been...
There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In addition to the inherent scientific interest of this question, these latent quantities are often invoked to explain the behavior of LLMs to users or to define and evalua...
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