# How LLMs Distort Our Written Language

arXiv: https://arxiv.org/abs/2603.18161 · HTML: https://mlsanity.com/paper/2603.18161/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2026.

## Built on by (most cited first, 21 of 21)

- [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026)
- [SPADE: Self-Play in Adaptive Synthetic Executable Environments](https://mlsanity.com/paper/2608.19197/) (2026)
- [Attractor States Emerge in Multi-Turn LLM Conversations](https://mlsanity.com/paper/2606.30571/) (2026)
- [AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing](https://mlsanity.com/paper/2603.23069/) (2026)
- [Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers](https://mlsanity.com/paper/2603.25638/) (2026)
- [How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions](https://mlsanity.com/paper/2604.22503/) (2026)
- [Argument Collapse: LLMs Flatten Long-Form Public Debate](https://mlsanity.com/paper/2606.01736/) (2026)
- [Solipsistic Superintelligence is Unlikely to be Cooperative](https://mlsanity.com/paper/2606.03237/) (2026)
- [The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models](https://mlsanity.com/paper/2604.19139/) (2026)
- [Voice Under Revision: Large Language Models and the Normalization of Personal Narrative](https://mlsanity.com/paper/2604.22142/) (2026)
- [Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences](https://mlsanity.com/paper/2606.07629/) (2026)
- [From 'May' to 'Is': Certainty Distortion in Language Model Rewriting](https://mlsanity.com/paper/2606.07951/) (2026)
- [Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing](https://mlsanity.com/paper/2606.15914/) (2026)
- [Pigeonholing: how bad prompts hurt models, causing collapse and mistakes](https://mlsanity.com/paper/2606.24267/) (2026)
- [Linguistic Monoculture in LLM-Assisted Language Use](https://mlsanity.com/paper/2607.27134/) (2026)
- [Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models](https://mlsanity.com/paper/2608.15129/) (2026)
- [Efficient Test-Time Adaptation through Human-AI Interaction](https://mlsanity.com/paper/2609.04141/) (2026)
- [Do Reviewers Still Reward Lexical Complexity? A Frozen-Rater Study of Preference Drift in 124K ICLR Reviews](https://mlsanity.com/paper/2609.08475/) (2026)
- [Gacha Decoding: Eliciting Diverse Generations Through Instruction Following](https://mlsanity.com/paper/2610.01382/) (2026)
- [Self-Consuming Generative Models with Co-Evolving Human Preferences](https://mlsanity.com/paper/2610.09415/) (2026)
- [Measuring and Mitigating Solution Mode Collapse in RLVR](https://mlsanity.com/paper/2610.11064/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that LLMs not only alter the voice and tone of human writing but also consistently alter the intended meaning. First, we conduct a human user study to understand how people actually interact with LLMs when using them for writing. Our findings reveal that extensive LLM use led to a nearly 70% increase in essays that remained neutral in answering the topic question. Significantly more heavy LLM users reported that the writing was less creative and not in their voice. Next, using a dataset of human-written essays that was collected in 2021 before the widespread release of LLMs, we study how asking an LLM to revise the essay based on the human-written feedback in the dataset induces large changes in the resulting content and meaning. We find that even when LLMs are prompted with expert feedback and asked to only make grammar edits, they still change the text in a way that significantly alters its semantic meaning. We then examine LLM-generated text in the wild, specifically focusing on the 21% of AI-generated scientific peer reviews at a recent top AI conference. We find that LLM-generated reviews place significantly less weight on clarity and significance of the research, and assign scores that, on average, are a full point higher. These findings highlight a misalignment between the perceived benefit of AI use and an implicit, consistent effect on the semantics of human writing, motivating future work on how widespread AI writing will affect our cultural and scientific institutions.