Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models

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. Recent leaps in the capabilities of large language models (LLMs) such as GPT-4 (OpenAI, 2023), LLaMA (Touvron et al., 2023), and Claude have driven a wave of interest in constructing the best prompt for a given task, where a prompt generally refers to an input to the LLM. Various prompting strategies have been proposed, including but not limited to: in-context learning (Brown et al., 2020) , instruction following (Wei et al., 2022a), chain-of-thought prompting (Wei et al., 2022b), Published as a conference paper at ICLR 2024. * indicates equal contribution.