  We introduce {\em generative monoculture}, a behavior observed in large
language models (LLMs) characterized by a significant narrowing of model output
diversity relative to available training data for a given task: for example,
generating only positive book reviews for books with a mixed reception. While
in some cases, generative monoculture enhances performance (e.g., LLMs more
often produce efficient code), the dangers are exacerbated in others (e.g.,
LLMs refuse to share diverse opinions). As LLMs are increasingly used in
high-impact settings such as education and web search, careful maintenance of
LLM output diversity is essential to ensure a variety of facts and perspectives
are preserved over time. We experimentally demonstrate the prevalence of
generative monoculture through analysis of book review and code generation
tasks, and find that simple countermeasures such as altering sampling or
prompting strategies are insufficient to mitigate the behavior. Moreover, our
results suggest that the root causes of generative monoculture are likely
embedded within the LLM's alignment processes, suggesting a need for developing
fine-tuning paradigms that preserve or promote diversity.
