  The recent explosion in the capabilities of large language models has led to
a wave of interest in how best to prompt a model to perform a given task. While
it may be tempting to simply choose a prompt based on average performance on a
validation set, this can lead to a deployment where unexpectedly poor responses
are generated, especially for the worst-off users. To mitigate this prospect,
we propose Prompt Risk Control, a lightweight framework for selecting a prompt
based on rigorous upper bounds on families of informative risk measures. We
offer methods for producing bounds on a diverse set of metrics, including
quantities that measure worst-case responses and disparities in generation
quality across the population of users. In addition, we extend the underlying
statistical bounding techniques to accommodate the possibility of distribution
shifts in deployment. Experiments on applications such as open-ended chat,
medical question summarization, and code generation highlight how such a
framework can foster responsible deployment by reducing the risk of the worst
outcomes.
