Estimating the Hallucination Rate of Generative AI
Authors: name: Andrew Jesson, name: Nicolas Beltran-Velez, name: Quentin Chu, name: Sweta Karlekar, name: Jannik Kossen, name: Yarin Gal, name: John P. Cunningham, name: David Blei

This work is about estimating the hallucination rate for in-context learning
(ICL) with Generative AI. In ICL, a conditional generative model (CGM) is
prompted with a dataset and asked to make a prediction based on that dataset.
The Bayesian interpretation of ICL assumes that the CGM is calculating a
posterior predictive distribution over an unknown Bayesian model of a latent
parameter and data. With this perspective, we define a hallucination
as a generated prediction that has low-probability under the true latent
parameter. We develop a new method that takes an ICL problem -- that is, a CGM,
a dataset, and a prediction question -- and estimates the probability that a
CGM will generate a hallucination. Our method only requires generating queries
and responses from the model and evaluating its response log probability. We
empirically evaluate our method on synthetic regression and natural language
ICL tasks using large language models.