  This paper presents a systematic defense of large language model (LLM)
hallucinations or 'confabulations' as a potential resource instead of a
categorically negative pitfall. The standard view is that confabulations are
inherently problematic and AI research should eliminate this flaw. In this
paper, we argue and empirically demonstrate that measurable semantic
characteristics of LLM confabulations mirror a human propensity to utilize
increased narrativity as a cognitive resource for sense-making and
communication. In other words, it has potential value. Specifically, we analyze
popular hallucination benchmarks and reveal that hallucinated outputs display
increased levels of narrativity and semantic coherence relative to veridical
outputs. This finding reveals a tension in our usually dismissive
understandings of confabulation. It suggests, counter-intuitively, that the
tendency for LLMs to confabulate may be intimately associated with a positive
capacity for coherent narrative-text generation.
