How LLMs Distort Our Written Language

arXiv:2603.18161 · cs.CL, cs.AI · Submitted 2026-03-18 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-03-18

Updated: 2026-08-26

Code: https://github.com/abdulhaim/llm_writing_distortion

Project page: https://sites.google.com/view/llmwritingdistortion/home

Terminology

• LLM-as-a-Judge — 14× in this paper · explained in 3 episodes
• Homogenization — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Generative AI — 5× in this paper · explained in 2 episodes
• Actor — 4× in this paper · explained in 3 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Jensen-Shannon Divergence — 4× in this paper
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Steering — 4× in this paper · explained in 2 episodes
• Accountability — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Homogeneity — 2× in this paper · explained in 2 episodes
• Institutional Review Board — 2× in this paper
• International Conference on Learning Representations — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• AI Alignment — 1× in this paper · explained in 2 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Linguistic Inquiry and Word Count — 1× in this paper
• Principal Component Analysis — 1× in this paper
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• AI Alignment with Changing and Influenceable Reward Functions
• ArgRewrite V.2: an Annotated Argumentative Revisions Corpus
• Generative artificial intelligence enhances creativity but reduces the diversity of novel content
• Technical Report on the Pangram AI-Generated Text Classifier
• Artificial intelligence in communication impacts language and social relationships
• The Lock-in Hypothesis: Stagnation by Algorithm
• Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
• Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
• Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
• Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization
• Mapping the Increasing Use of LLMs in Scientific Papers
• Efficient Estimation of Word Representations in Vector Space
• Training language models to follow instructions with human feedback
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
• Human Decision-making is Susceptible to AI-driven Manipulation
• Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF
• A Roadmap to Pluralistic Alignment
• The Homogenizing Effect of Large Language Models on Human Expression and Thought
• Empirical evidence of Large Language Model's influence on human spoken communication
• The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices

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