title: Estimating the Hallucination Rate of Generative AI
summary: 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.
paper_authors._id: 666c64b370f5a2cb4a5fd753
paper_authors.name: Andrew Jesson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-14T15:45:25.211Z
paper_authors._id: 666a65a66cf1faccbd4c85ea
paper_authors.avatarUrl: /avatars/97262276275537ce3926efd93ac5a086.svg
paper_authors.fullname: andrew jesson
paper_authors.type: user
paper_authors.user: anndvision
paper_authors._id: 666c64b370f5a2cb4a5fd754
paper_authors.name: Nicolas Beltran-Velez
paper_authors._id: 666c64b370f5a2cb4a5fd755
paper_authors.name: Quentin Chu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-14T15:45:38.744Z
paper_authors._id: 65089ef1ce83a0c12a664e33
paper_authors.avatarUrl: /avatars/2af4ffd863142e1dbccfedab782d7ab0.svg
paper_authors.fullname: Quentin Chu
paper_authors.type: user
paper_authors.user: qc2321
paper_authors._id: 666c64b370f5a2cb4a5fd756
paper_authors.name: Sweta Karlekar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-14T15:45:53.881Z
paper_authors._id: 65e0dcaf35191b15a3873835
paper_authors.avatarUrl: /avatars/65d44808417b133cf83216122a1b511e.svg
paper_authors.fullname: Sweta Karlekar
paper_authors.type: user
paper_authors.user: swkarlekar
paper_authors._id: 666c64b370f5a2cb4a5fd757
paper_authors.name: Jannik Kossen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-14T15:45:46.815Z
paper_authors._id: 64df30f2e672e4d2c57049e8
paper_authors.avatarUrl: /avatars/8c9503e43f403d655b11ea5c6e23ae79.svg
paper_authors.fullname: Jannik
paper_authors.type: user
paper_authors.user: jlko
paper_authors._id: 666c64b370f5a2cb4a5fd758
paper_authors.name: Yarin Gal
paper_authors._id: 666c64b370f5a2cb4a5fd759
paper_authors.name: John P. Cunningham
paper_authors._id: 666c64b370f5a2cb4a5fd75a
paper_authors.name: David Blei
paper_title: Estimating the Hallucination Rate of Generative AI
paper_summary: 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.
paper_ai_keywords: in-context learning (ICL) conditional generative model (CGM) Bayesian interpretation posterior predictive distribution latent hallucination specific posterior predictive distribution generated prediction latent parameter low-probability under the true latent parameter