AI 中文总结
该研究针对LLM辅助语言使用中的语言单一文化问题，构建数学框架分析作者与LLM的共同演化机制，发现个性化可保留语言多样性，且个体理性作者的过度一致性会产生负外部性。
AI 中文摘要
写作与沟通日益由大型语言模型（LLM）介导，这些模型被用于起草、修改和润色文本。尽管此类辅助可提升清晰度并帮助作者满足机构期望，但广泛依赖共享模型可能会减少人口层面的语言形式变异，我们将这一现象称为语言单一文化。我们构建了一个数学框架，其中作者和LLM被表示为语言特征分布，并通过反复互动共同演化。我们分析了三种互动机制：具有固定语言分布的共享模型、根据作者输出递归更新的共享模型，以及通过作者特定和人口层面反馈更新的个性化模型。我们刻画了由此产生的均衡与收敛速率，结果显示：共享模型可推动作者趋向共同规范，递归反馈会在共同一致性下改变共享规范的位置但不改变成对扩散，而个性化可保留一系列具有非零语言多样性的独特作者-模型均衡。随后，我们将一致性内化为一种策略选择，权衡清晰度、易读性和感知流畅性带来的私人收益与独特风格。在这一效用模型中，个体理性作者的一致性程度可能超过社会最优水平，因为他们未将自身独特性为他人提供的价值内部化，从而产生负外部性和单一文化代价——该代价对每个固定实例是有限的，但当独特性主导真实性时可无限增长。综合模拟展示了固定共享辅助、递归反馈和个性化如何产生不同的长期多样性结果。
英文摘要
Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although such assistance can improve clarity and help authors meet institutional expectations, widespread reliance on shared models may reduce population-level variation in linguistic form, a phenomenon we refer to as linguistic monoculture. We develop a mathematical framework in which authors and LLMs are represented as distributions over linguistic features and coevolve through repeated interaction. We analyze three interaction mechanisms: a shared model with a fixed linguistic distribution, a shared model recursively updated from author outputs, and personalized models updated through author-specific and population-level feedback. We characterize the resulting equilibria and convergence rates, showing that, shared models can drive authors toward a common norm, recursive feedback relocates the shared norm without altering pairwise spread under common conformity, and personalization can preserve a family of distinct author-model equilibria with nonzero linguistic diversity. We then endogenize conformity as a strategic choice trading off private benefits from clarity, legibility, and perceived fluency against distinctive style. Within this utility model, individually rational authors may conform more than is socially optimal because they do not internalize the value their distinctiveness provides to others, creating a negative externality and a price of monoculture that is finite for each fixed instance but can grow without bound when distinctiveness dominates authenticity. Synthetic simulations illustrate how fixed shared assistance, recursive feedback, and personalization produce different long-run diversity outcomes.
