title: How Language Model Hallucinations Can Snowball
summary: A major risk of using language models in practical applications is their
  tendency to hallucinate incorrect statements. Hallucinations are often
  attributed to knowledge gaps in LMs, but we hypothesize that in some cases,
  when justifying previously generated hallucinations, LMs output false claims
  that they can separately recognize as incorrect. We construct three
  question-answering datasets where ChatGPT and GPT-4 often state an incorrect
  answer and offer an explanation with at least one incorrect claim. Crucially,
  we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes,
  respectively. We refer to this phenomenon as hallucination snowballing: an LM
  over-commits to early mistakes, leading to more mistakes that it otherwise
  would not make.
paper_authors._id: 646d5e70bb95b5d4001df19f
paper_authors.name: Muru Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-24T13:39:14.401Z
paper_authors._id: 61cc2cf4dcb47bd5ed3cd3b8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1640770780085-noauth.jpeg
paper_authors.fullname: Muru Zhang
paper_authors.type: user
paper_authors.user: nanami
paper_authors._id: 646d5e70bb95b5d4001df1a0
paper_authors.name: Ofir Press
paper_authors._id: 646d5e70bb95b5d4001df1a1
paper_authors.name: William Merrill
paper_authors._id: 646d5e70bb95b5d4001df1a2
paper_authors.name: Alisa Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-24T13:39:52.972Z
paper_authors._id: 62449da46b55ef458d430371
paper_authors.avatarUrl: /avatars/b95954a88c7b15524a3d7776a4abe083.svg
paper_authors.fullname: Alisa Liu
paper_authors.type: user
paper_authors.user: alisawuffles
paper_authors._id: 646d5e70bb95b5d4001df1a3
paper_authors.name: Noah A. Smith
paper_title: How Language Model Hallucinations Can Snowball
paper_summary: A major risk of using language models in practical applications is their
  tendency to hallucinate incorrect statements. Hallucinations are often
  attributed to knowledge gaps in LMs, but we hypothesize that in some cases,
  when justifying previously generated hallucinations, LMs output false claims
  that they can separately recognize as incorrect. We construct three
  question-answering datasets where ChatGPT and GPT-4 often state an incorrect
  answer and offer an explanation with at least one incorrect claim. Crucially,
  we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes,
  respectively. We refer to this phenomenon as hallucination snowballing: an LM
  over-commits to early mistakes, leading to more mistakes that it otherwise
  would not make.
paper_ai_keywords: language models hallucinations knowledge gaps question-answering datasets ChatGPT GPT-4 hallucination snowballing