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Reliable decision-making requires more than accurate prediction: a model must preserve its beliefs, apply the relevant utilities, and recognize when the information needed to justify an action is missing. We study whether language models can learn this decision procedure from supervised fine-tuning and generalize it ac...
It is suggested that language models for high-stakes applications should be understood and evaluated as probabilistic decision systems whose recommendations depend jointly on predictive performance and explicit utilities.
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...
An empirical pipeline is presented for estimating the implied preferences that an LLM's observed choices optimize: the model's probability distribution over unknowns is elicited along with the choice it would make for the decision task and a discrete choice model is fit to recover the cost function that best rationaliz...
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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