Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models

As Large Language Models (LLMs) and their capabilities become an increasingly prominent aspect of our workflows and our lives, it is important that we are thoughtful and deliberate with the words we use to refer to the inner workings and outputs of this technology.We think that conveying the complex functions (and malfunctions) of LLMs using metaphorical language that is precise and accurate can lead to a better understanding of these powerful tools among both the academic community and the public.If we are meticulous in our choice of metaphors, we open ourselves up to the possibility of achieving a better shared understanding of the complex concepts in this exciting new field.Here, we give the specific example of AI "hallucinations" and demonstrate how a change in metaphorical language can lead to new ways of understanding and may even foreshadow future directions in the development of artificial intelligence systems.In psychiatry, hallucinations are a relatively well-defined perceptual phenomenon referring to sensory experiences without associated external, or 'real-world' stimuli.Clinically, hallucinations are commonly associated with conditions such as schizophrenia, bipolar disorder, and Parkinson's disease [1].Employing the term "hallucination" to characterize the inaccurate and non-factual outputs generated by LLMs implies acceptance of the notion that LLMs are engaged in perceiving, that is, becoming consciously aware of a sensory input.While this is a subject of some ongoing debate, there is currently no evidence that AI has gained conscious awareness [2].LLMs do not have sensory experiences, and thus cannot mistakenly perceive them as real.As such, we believe the term "hallucination" misrepresents the nature of the process occurring within LLMs which it has been used to describe.The model is not "seeing" something that is not there, but it is making things up.More accurate terminology is found in the psychiatric concept of confabulation, which refers to the generation of narrative details that, while incorrect, are not recognized as such.Unlike hallucinations, confabulations are not perceived experiences but instead mistaken reconstructions of information which are influenced by existing knowledge, experiences, expectations, and context.Confabulation can occur in various clinical conditions including dementia, Wernicke-Korsakoff's syndrome, schizophrenia, traumatic brain injury (TBI), and cerebrovascular accidents (CVAs) [3].Confabulation is frequently associated with a generalised lack of awareness of one's deficits often seen in right sided CVAs or TBIs, as well as in bipolar disorder, schizophrenia, and the dementias [4].When answering questions, LLMs generate responses based on learned patterns in very large datasets [5].The output of LLMs can

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