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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.
Large language and multimodal models have been largely employed to reactively generate content and assistance. In contrast, we explore how models can be employed to provide proactive assistance that fluidly supports ongoing human activities. We identify and tackle challenges in such streaming scenarios—where models mus...
This work proposes a set of aspirational directions for guiding the behavior of general-purpose AI systems in ways that may reduce potential psychological harms and support user well-being, and identifies open questions and areas requiring deeper study.
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