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Hallucination (artificial intelligence)

Erroneous AI-generated content presented as true

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Chatbots powered by large language models (LLMs), like ChatGPT, may embed plausible-sounding random falsehoods within their generated content, such as fabricated citations. Detecting and mitigating errors and hallucinations pose significant challenges for practical deployment and reliability of LLMs in high-stakes scenarios, such as chip design, supply chain logistics, and medical diagnostics. Some software engineers and statisticians have criticized the specific term "AI hallucination" for unreasonably anthropomorphizing computers. Symbolic artificial intelligence models generally do not produce hallucinations, unlike large language models.

Term

Origin

In computer vision, since the 2000s, hallucination is adding detail to an image to improve it. For example, the task of generating high-resolution face images from low-resolution inputs is face hallucination. The term was introduced in the PhD thesis of Eric Mjolsness in 1986. A notable work is the face hallucination algorithm by Simon Baker and Takeo Kanade published in 1999.

In the 2000s, hallucinations were described in statistical machine translation as a failure mode.

Since the 2010s, hallucination has come to mean the generation of factually incorrect or misleading outputs by AI systems in tasks like machine translation and object detection. In 2015, hallucinations were identified in visual semantic role labeling tasks by Saurabh Gupta and Jitendra Malik. In 2015, computer scientist Andrej Karpathy used hallucinated in a blog post to describe his recurrent neural network (RNN) language model generating an incorrect citation link. In 2017, Google researchers used it to describe the responses generated by neural machine translation (NMT) models when they are not related to the source text, and in 2018, it was used in computer vision to describe the erroneous detection of non-existent objects through adversarial attacks.

In July 2021, Meta warned during its release of BlenderBot 2 that the system is prone to hallucinations, defined as "confident statements that are not true". Following OpenAI's ChatGPT release in beta version in November 2022, some users complained that such chatbots often seem to pointlessly embed plausible-sounding random falsehoods within their generated content. Many news outlets, including The New York Times, started to use hallucinations to describe these models' frequently incorrect or inconsistent responses.

In 2023, the Cambridge dictionary updated its definition of hallucination to include this new sense specific to the field of AI.

Some researchers have highlighted a lack of consistency in how hallucination is used, but also identified several alternative terms in the literature, such as confabulations, fabrications, and factual errors.

Definitions and alternatives

Uses, definitions and characterizations of the term hallucination in the context of LLMs include:

  • "a tendency to invent facts in moments of uncertainty" (OpenAI, May 2023)
  • "a model's logical mistakes" (OpenAI, May 2023)
  • "fabricating information entirely, but behaving as if spouting facts" (CNBC, May 2023)
  • "making up information" (The Verge, February 2023)
  • "misleading and/or nonsensical factual errors" (Algorithm Watch and AI Forensics, December 2023)
  • "probability distributions" (in scientific contexts)

In July 2024, a White House report on fostering public trust in AI research mentioned hallucinations only in the context of reducing them. Notably, when acknowledging David Baker's Nobel Prize-winning work with AI-generated proteins, the Nobel committee avoided it entirely, instead referring to "imaginative protein creation".

Hicks, Humphries, and Slater, in their article in Ethics and Information Technology, argue that the output of LLMs is bullshit under Harry Frankfurt's definition, and that the models are "in an important way indifferent to the truth of their outputs", with true statements only accidentally true, and false ones accidentally false. Some researchers also call the uncritical use of AI botshit.

The term mirage has been proposed as an alternative to the use of hallucination, framing false or misleading responses as predictable results of LLM data processing created by both AI training datasets and the rules engineered into the LLM rather than a mental breaking from reality. This shift in language aims to increase AI literacy by using terminology that avoids anthropomorphic implications that LLMs have conscious minds with their own intent, shows that untrue outputs come from patterns in training data, and indicates that outputs are novel syntheses of training data requiring critical human judgment.

Criticism

In the scientific community, some researchers avoid hallucination, as potentially misleading. Usama Fayyad, executive director of the Institute for Experimental Artificial Intelligence at Northeastern University, criticized it because it misleadingly personifies large language models and is vague. The computer scientist Mary Shaw has said "The current fashion for calling generative AI's errors 'hallucinations' is appalling. It anthropomorphizes the software, and it spins actual errors as somehow being idiosyncratic quirks of the system even when they're objectively incorrect." The statistician Gary Smith argues that LLMs "do not understand what words mean" and consequently that hallucination unreasonably anthropomorphizes the machine. Murray Shanahan argues that anthropomorphic framing of LLM capabilities, including terms like hallucination, encourages users and researchers to attribute cognitive processes to systems that operate through statistical pattern completion, and advocates more careful linguistic practices when discussing LLM behavior. Kristina Šekrst argues that applying psychological vocabulary to LLM outputs obscures the difference between the appearance of mental properties and their genuine presence. Förster & Skop assert that tech companies use the hallucination metaphor to anthropomorphize models and deflect responsibility for non-factual outputs. In January 2026, researchers Emily M. Bender and Nanna Inie broadly criticized the anthropomorphization of AI in an op-ed, later proposing "undesirable output" as a linguistic alternative.

Some see the AI outputs not as illusory but as prospective—that is, having some chance of being true, similar to early-stage scientific conjectures. The term has also been criticized for its association with psychedelic drug experiences.

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