LLMs Generate Kitsch
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 1 Apr 2026.
Builds on
9 of 38 references.
References · 9
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2019The Curious Case of Neural Text Degenerationcited by 2,916
- 2025The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideascited by 62
- 2025The Widespread Adoption of Large Language Model-Assisted Writing Across Societycited by 51
- 2025Who is using AI to code? Global diffusion and impact of generative AIcited by 18
- 2025A Review of LLM-Assisted Ideationcited by 7
- 2025The Even Sheen of AI: Kitsch, LLMs, and Homogeneitycited by 1
- 2026Bizarre Love Triangle: Generative AI, Art, and Kitschcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Do Large Language Models Always Tell The Same Stories?cited by 1
- 2026Do Large Language Models Always Tell The Same Stories?15 Jun 2026