When the Feature Pool Goes Algorithmic: Extending Mufwene's Ecology of Language Evolution to LLM-Mediated Exposure

• URL: http://arxiv.org/abs/2608.21088v1
• Date: Fri, 21 Aug 2026 13:32:26 GMT
• Title: When the Feature Pool Goes Algorithmic: Extending Mufwene's Ecology of Language Evolution to LLM-Mediated Exposure
• Abstract summary: Mufwene's ecological model locates language evolution in competition among variants contributed by individual idiolects.<n>Large language models (LLMs) complicate this architecture without requiring the locus of selection to move away from human speakers.<n>I call the resulting ecological process algorithmic reweighting of the speaker-accessible distribution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Linguistic Monoculture in LLM-Assisted Language Use [6.525195905471158]
  We develop a mathematical framework in which authors and large language models are represented as distributions over linguistic features.<n>We characterize the resulting equilibria and convergence rates, showing that, shared models can drive authors toward a common norm.<n>We then endogenize conformity as a strategic choice trading off private benefits from clarity, legibility, and perceived fluency against distinctive style.
  arXiv  Detail & Related papers  (2026-07-29T17:04:36Z)
• Task Ecologies and the Evolution of World-Tracking Representations in Large Language Models [0.0]
  We study language models as evolving model organisms and ask when autoregressive next-token learning selects for world-tracking representations.<n>For any encoding of latent world states, the Bayes-optimal next-token cross-entropy decomposes into the irreducible conditional entropy plus a Jensen--Shannon excess term.<n>This yields a precise notion of ecological veridicality for language models.
  arXiv  Detail & Related papers  (2026-04-07T06:06:49Z)
• Tokenization and Morphological Fidelity in Uralic NLP: A Cross-Lingual Evaluation [9.23725598061561]
  This study systematically compares three subword paradigms -- Byte Pair.<n>(BPE), Overlap BPE (OBPE), and Unigram Language Model -- across six Uralic languages.<n>We show OBPE consistently achieves stronger morphological alignment and higher tagging accuracy than conventional methods.
  arXiv  Detail & Related papers  (2026-02-04T05:59:25Z)
• A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors [50.046717886067555]
  We show that when sampling corpora from an aligned language model, there exists a trade-off between the strings' average reward and average log-likelihood. We provide a formal treatment of this phenomenon and demonstrate how a choice of sampling adaptor allows for a selection of how much likelihood we exchange for the reward.
  arXiv  Detail & Related papers  (2024-06-14T17:38:21Z)
• SpeechAlign: Aligning Speech Generation to Human Preferences [51.684183257809075]
  We introduce SpeechAlign, an iterative self-improvement strategy that aligns speech language models to human preferences. We show that SpeechAlign can bridge the distribution gap and facilitate continuous self-improvement of the speech language model.
  arXiv  Detail & Related papers  (2024-04-08T15:21:17Z)
• BrainLLM: Generative Language Decoding from Brain Recordings [77.66707255697706]
  We propose a generative language BCI that utilizes the capacity of a large language model and a semantic brain decoder.<n>The proposed model can generate coherent language sequences aligned with the semantic content of visual or auditory language stimuli.<n>Our findings demonstrate the potential and feasibility of employing BCIs in direct language generation.
  arXiv  Detail & Related papers  (2023-11-16T13:37:21Z)
• Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
  We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
  arXiv  Detail & Related papers  (2023-10-10T03:06:38Z)
• Coherent Wave Dynamics and Language Generation of a Generative Pre-trained Transformer [0.7832189413179361]
  We analyze the hidden state and channel wave dynamics in a small Generative Pretrained Transformer (GPT) Our findings suggest that wave dynamics offer consistent and repeatable intrinsic oscillation modes, along with context-aware plasticity and expressiveness in language generation. In addition, we investigate the Poisson statistics of spelling errors in text sequence generation across various levels of model training.
  arXiv  Detail & Related papers  (2023-05-08T21:35:12Z)
• Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem [65.25725367771075]
  This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized.
  arXiv  Detail & Related papers  (2021-12-17T08:35:40Z)
• Factorized Neural Transducer for Efficient Language Model Adaptation [51.81097243306204]
  We propose a novel model, factorized neural Transducer, by factorizing the blank and vocabulary prediction. It is expected that this factorization can transfer the improvement of the standalone language model to the Transducer for speech recognition. We demonstrate that the proposed factorized neural Transducer yields 15% to 20% WER improvements when out-of-domain text data is used for language model adaptation.
  arXiv  Detail & Related papers  (2021-09-27T15:04:00Z)
• Evaluating Models of Robust Word Recognition with Serial Reproduction [8.17947290421835]
  We compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. We find that those models that make use of abstract representations of preceding linguistic context best predict the changes made by people in the course of serial reproduction.
  arXiv  Detail & Related papers  (2021-01-24T20:16:12Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.