AI Hallucinations: A Misnomer Worth Clarifying

TL;DR

This paper addresses the fragmented use of the term AI hallucination across domains by conducting a broad systematic literature review across fourteen databases to catalog definitions and alternative terms. It reveals a lack of a formal, universal definition and highlights substantial domain-specific variation in the characteristics ascribed to hallucinations, including intrinsic/extrinsic and fidelity-focused distinctions. The authors advocate for terminological standardization and a formal, cross-domain taxonomy to improve clarity and reduce stigma when discussing AI-generated content. The work emphasizes practical impact across medicine, law, ethics, and technology, and calls for future consensus-building to harmonize definitions and reporting practices.

Abstract

As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon termed often as "hallucination." However, with AI's increasing presence across various domains including medicine, concerns have arisen regarding the use of the term itself. In this study, we conducted a systematic review to identify papers defining "AI hallucination" across fourteen databases. We present and analyze definitions obtained across all databases, categorize them based on their applications, and extract key points within each category. Our results highlight a lack of consistency in how the term is used, but also help identify several alternative terms in the literature. We discuss implications of these and call for a more unified effort to bring consistency to an important contemporary AI issue that can affect multiple domains significantly.