LLMs Generate Kitsch

arXiv cs.CL / 4/30/2026

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Key Points

  • The paper argues that large language models (LLMs) tend to produce “kitsch” (synthetically artificial, generic-feeling creative outputs) as a systematic outcome of their training process.
  • Controlled studies suggest LLM-generated works can score higher than human-made ones, yet they may still feel generic and emotionally hollow to audiences.
  • The authors present empirical evidence that readers rate LLM-generated stories as kitschier, even when controlling for how “kitsch” is defined.
  • The research discusses how this phenomenon should shape future study designs and affect how we think about using LLMs for creative tasks like writing, research, and coding.
  • Overall, it highlights a tension between measurable quality and perceived originality/meaning in AI-generated creativity.

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.