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Found 3 papers, 2 papers with code
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.
Distribution-Free Statistical Dispersion Control for Societal Applications
Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning.
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor.
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