Hallucination (artificial intelligence)

A screenshot from a video generated by artificial intelligence Sora. The image contains a mistake: it shows the Glenfinnan Viaduct, a famous bridge, but with an extra train track added that is not there in reality. The train itself resembles a real train called The Jacobite, but it has an extra chimney that should not be there.

In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called confabulation[1] or delusion[2]) is a response generated by AI which contains false or misleading information presented as fact.[3][4][5] This term draws a loose analogy with human psychology, where hallucination typically involves false percepts. However, there is a key difference: AI hallucination is associated with unjustified responses or beliefs rather than perceptual experiences.[5]

For example, a chatbot powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within its generated content. Researchers have recognized this issue, and by 2023, analysts estimated that chatbots hallucinate as much as 27% of the time, with factual errors present in 46% of their responses. Detecting and mitigating these hallucinations pose significant challenges for practical deployment and reliability of LLMs in real-world scenarios.[6][7][8] Some researchers believe the specific term "AI hallucination" unreasonably anthropomorphizes computers.[1]

  1. ^ a b Edwards, Benj (6 April 2023). "Why ChatGPT and Bing Chat are so good at making things up". Ars Technica. Retrieved 11 June 2023.
  2. ^ "Shaking the foundations: delusions in sequence models for interaction and control". www.deepmind.com. 22 December 2023.
  3. ^ "Definition of HALLUCINATION". www.merriam-webster.com. 21 October 2023. Retrieved 29 October 2023.
  4. ^ Joshua Maynez; Shashi Narayan; Bernd Bohnet; Ryan McDonald (2020). "On Faithfulness and Factuality in Abstractive Summarization". Proceedings of The 58th Annual Meeting of the Association for Computational Linguistics (ACL) (2020). arXiv:2005.00661. Retrieved 26 September 2023.
  5. ^ a b Ji, Ziwei; Lee, Nayeon; Frieske, Rita; Yu, Tiezheng; Su, Dan; Xu, Yan; Ishii, Etsuko; Bang, Yejin; Dai, Wenliang; Madotto, Andrea; Fung, Pascale (November 2022). "Survey of Hallucination in Natural Language Generation" (pdf). ACM Computing Surveys. 55 (12). Association for Computing Machinery: 1–38. arXiv:2202.03629. doi:10.1145/3571730. S2CID 246652372. Retrieved 15 January 2023.
  6. ^ Cite error: The named reference cnbc several errors was invoked but never defined (see the help page).
  7. ^ Metz, Cade (6 November 2023). "Chatbots May 'Hallucinate' More Often Than Many Realize". The New York Times.
  8. ^ de Wynter, Adrian; Wang, Xun; Sokolov, Alex; Gu, Qilong; Chen, Si-Qing (13 July 2023). "An evaluation on large language model outputs: Discourse and memorization". Natural Language Processing Journal. 4. arXiv:2304.08637. doi:10.1016/j.nlp.2023.100024. ISSN 2949-7191.

© MMXXIII Rich X Search. We shall prevail. All rights reserved. Rich X Search