An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and…

The phenomenon described, where a large language model (LLM) generates content that is grammatically correct, plausible, and confident-sounding but is factually incorrect or nonsensical, is known as hallucination. This occurs when the model generates information that is not grounded in its training data or a provided source context. In the context of marketing, this could lead to the creation of campaigns with false claims or inaccurate product details, even though the text itself reads professionally.

1. Ji

Z.

Lee

N.

Frieske

R.

Yu

T.

Su

D.

Xu

Y.

... & Fung

P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys

55(12)

1-38. In Section 1

the paper defines hallucination as "generated content that is nonsensical or unfaithful to the provided source content." (DOI: https://doi.org/10.1145/3571730)

2. Stanford University Human-Centered AI (HAI). (2023

August 14). What Are AI Hallucinations? The article states

"In the context of large language models

a hallucination is a confident response by an AI that does not seem to be justified by its training data." This directly aligns with the scenario of a plausible but incorrect output.

3. Google Cloud. (2023). Introduction to Generative AI: Language. In the section on "LLM limitations

" it is noted that models can "generate text that sounds plausible but is factually incorrect. This behavior is often referred to as hallucination or confabulation."