High-stakes decisions under uncertainty, such as medical emergency triage, require more than accurate predictions. They depend on estimating the likelihood of alternative outcomes while explicitly weighing the consequences of different actions, principles that have long formed the foundation of medical diagnosis and de...
K. Yamin, Christopher Kelly, Bryan Wilder et al.· 1 citation
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...
K. Yamin, Jingjing Tang, Eric Horvitz et al.· arXiv.org· 4 citations
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...
Alex Smolin, Bryan Wilder· 0 citations
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