LLMs Generate Kitsch

TL;DR

This paper argues that Large Language Models tend to generate kitsch due to their training, affecting perceptions of creativity and originality in AI-generated content.

Contribution

It introduces the idea that LLMs systematically produce kitsch and empirically demonstrates how this influences human perception of AI-generated works.

Findings

Readers perceive LLM-generated stories as more kitsch when controlled for their definition.

LLMs' training process contributes to the generation of kitsch.

Implications for future research and creative applications are discussed.

Abstract

Large Language Models (LLMs) are increasingly used to generate pictures, texts, music, videos, and other works that have traditionally required human creativity. LLM-generated artifacts are often rated better than human-generated works in controlled studies. At the same time, they can come across as generic and hollow. We propose to resolve this tension by arguing that LLMs systematically generate kitsch, and that this is a consequence of the way in which they are trained. We also show empirically that readers perceive LLM-generated stories as kitschier, if we control for their definition of "kitsch". We discuss implications for the design of future studies and for creative tasks such as research and coding.