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.

翻译：本文系统论证了大型语言模型（LLM）的"幻觉"或"虚构"现象——将其视为潜在资源，而非全然负面的缺陷。主流观点认为虚构本质上是存在问题的，人工智能研究应当消除这一缺陷。然而本文通过理论与实证表明，LLM虚构的可测量语义特征，折射出人类利用增强叙事性作为认知资源进行意义建构与沟通的本能倾向。换言之，这种现象具有潜在价值。具体而言，我们通过分析主流幻觉基准测试发现，相较于真实输出，幻觉输出展现出更高水平的叙事性与语义连贯性。这一发现揭示了当前对虚构问题通常持否定态度的认知困境，并反直觉地表明：LLM产生虚构的倾向，可能与其生成连贯叙事文本的积极能力存在密切关联。