LLMs Generate Kitsch
For researchers and practitioners in AI and creative fields, this work provides a theoretical and empirical lens to understand the generic quality of LLM outputs, though the findings are incremental.
The paper argues that LLMs systematically generate kitsch due to their training process, and empirically shows that readers perceive LLM-generated stories as kitschier when controlling for the definition of 'kitsch'.
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.