AI 中文总结
本文将穆夫温的语言进化生态学扩展至LLM介导的场景，提出LLM作为分布中介通过算法重加权变体频率影响语言进化，且人类社会评价仍具决定性，同时产出多项可检验预测。
AI 中文摘要
穆夫温的生态模型将语言进化定位于个体方言变体间的竞争，以及说话者从互动提供的语言材料中进行的选择。大语言模型（LLM）使这一架构复杂化，但无需将选择的位点从人类说话者处移开。本文主张应将LLM视为分布中介：它们聚合人类群体产生的语言，通过训练和训练后处理改变其分布，并大规模重新分发模型特定的输出。本文将由此产生的生态过程称为说话者可及分布的算法重加权：模型介导可改变竞争变体到达人类选择者的相对频率。关于模型特定语言特征和词汇吸收的新兴证据与该路径的部分内容一致，但未确立必然的趋同。人类社会评价仍然具有决定性：与模型相关的形式可能扩散并成为常规化形式，被社会识别为“类AI”形式而随后被避免，或从一开始就无法扩散。该提案将穆夫温的特征池生态向说话者选择的上游扩展了一步，并产生关于词汇吸收、模型版本效应、趋同及社会逆转的可检验预测。
英文摘要
Mufwene's ecological model locates language evolution in competition among variants contributed by individual idiolects and in speakers' selection from linguistic material made available through interaction. Large language models (LLMs) complicate this architecture without requiring the locus of selection to move away from human speakers. This article argues that LLMs are best treated as distributional mediators: they aggregate language produced across human populations, transform its distribution through training and post-training, and redistribute model-specific outputs at scale. I call the resulting ecological process algorithmic reweighting of the speaker-accessible distribution: model mediation can alter the relative frequencies with which competing variants reach human selectors. Emerging evidence on model-specific linguistic profiles and lexical uptake is consistent with parts of this pathway, but does not establish inevitable convergence. Human social evaluation remains decisive: model-associated forms may diffuse and become conventionalized, become socially recognizable as 'AI-like' and subsequently avoided, or fail to diffuse in the first place. The proposal extends Mufwene's feature-pool ecology one step upstream of speaker selection and yields testable predictions about uptake, model-version effects, convergence, and social reversal.
