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.

通过分析图书评论和代码生成任务，我们实验证明了生成性单一文化的普遍存在，并发现简单的对策如改变采样或提示策略无法缓解这种行为。此外，我们的结果表明生成性单一文化的根本原因可能内在于语言模型的对准过程，因此需要开发能够保持或推广多样性的微调范例。

大型语言模型中的生成单一文化

Generative Monoculture in Large Language Models

This study investigates a range of psychological, lexical, semantic, and
readability features of book reviews to elucidate the factors underlying their
perceived popularity. To this end, we conduct statistical analyses of various
features, including the types and frequency of opinion and emotion-conveying
terms, connectives, character mentions, word uniqueness, commonness, and
sentence structure, among others. Additionally, we utilize two readability
tests to explore whether reading ease is positively associated with review
popularity. Finally, we employ traditional machine learning classifiers and
transformer-based fine-tuned language models with n-gram features to
automatically determine review popularity. Our findings indicate that, with the
exception of a few features (e.g., review length, emotions, and word
uniqueness), most attributes do not exhibit significant differences between
popular and non-popular review groups. Furthermore, the poor performance of
machine learning classifiers using the word n-gram feature highlights the
challenges associated with determining popularity in creative domains. Overall,
our study provides insights into the factors underlying review popularity and
highlights the need for further research in this area, particularly in the
creative realm.

本研究通过对书评的心理、词汇、语义、易读性等特征进行统计分析，使用两种易读性测试探索阅读便利程度与书评受欢迎程度的正向关联，并运用传统机器学习分类器和基于 Transformer 的预训练语言模型，自动判断书评的受欢迎程度。研究结果表明，除了一些特征（例如书评长度、情感和词语独特性）之外，大多数属性在受欢迎和不受欢迎的书评组之间没有显著差异。此外，使用词语 N-gram 特征的机器学习分类器的低性能凸显了在判断创意领域受欢迎程度方面所面临的挑战。总体而言，本研究揭示了影响书评受欢迎程度的因素，并强调了进一步研究的需要，尤其是在创意领域。