English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The recent explosion in the capabilities of large language models has led toa wave of interest in how best to prompt a model to perform a given task. Whileit may be tempting to simply choose a prompt based on average performance on avalidation set, this can lead to a deployment where unexpectedly poor responsesare generated, especially for the worst-off users. To mitigate this prospect,we propose Prompt Risk Control, a lightweight framework for selecting a promptbased on rigorous upper bounds on families of informative risk measures. Weoffer methods for producing bounds on a diverse set of metrics, includingquantities that measure worst-case responses and disparities in generationquality across the population of users. In addition, we extend the underlyingstatistical bounding techniques to accommodate the possibility of distributionshifts in deployment. Experiments on applications such as open-ended chat,medical question summarization, and code generation highlight how such aframework can foster responsible deployment by reducing the risk of the worstoutcomes.

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