Seonglae ChoWhen LLMs are used as writing-assistance tools, they do more than introduce superficial stylistic changes—they can systematically distort the intended meaning itself. This is quantified through three independent studies (an RCT user study, counterfactual editing analysis, and an ICLR peer-review analysis). In a randomized controlled trial with 100 participants, people wrote an essay on “Does money lead to happiness?”, comparing a condition with access to an LLM (gpt-4o-mini) against a control condition. The authors visualized semantic shifts using MiniLM-L6-v2 sentence embeddings with t-SNE/PCA, and measured differences in lexical distributions via Jensen–Shannon Divergence (JSD). They also quantified emotional and psychological language shifts using the NRC Emotion Lexicon and LIWC. Across edits, LLM-assisted revisions produced large semantic moves in a consistent direction in embedding space—contrasting with human edits, which tend to be smaller and more multi-directional.
Even when the prompt asks only for grammar correction, the LLM tends to change the essay’s conclusion. In the heavy-LLM-use group, the share of essays taking a neutral stance increased by 68.9% (). Participants also reported significantly lower creativity (, ) and a loss of personal voice (, ). On ArgRewrite-v2, the LLM’s JSD was nearly 3× the human baseline (0.2–0.3), reaching an average of 0.476; pronoun usage decreased by 40–60% while adjective usage increased by 57–90%. Positive sentiment increased by 37–54% and negative sentiment by 24–38%, indicating a general amplification of affective language. In an analysis of 18,000 ICLR 2026 peer reviews, LLM-generated reviews had scores about 10% higher on average than human reviews (4.43 vs 4.13); they were 32% less likely to cite clarity as a strength (, ), and 136% more likely to mention reproducibility.
How LLMs Distort Our Written Language
Executive Summary LLMs are used by over a billion people globally, and the most frequent use case is to assist with writing. LLMs can provide a huge efficiency boost, but are they actually writing what we want? Many users recognize the "feel" of LLM prose, but few people realize the extent to
https://sites.google.com/view/llmwritingdistortion/home
How LLMs Distort Our Written Language
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...
https://arxiv.org/abs/2603.18161
Analysing the impact of ChatGPT in research
Analysing the impact of ChatGPT in research
Applied Intelligence - Large Language Models (LLMs) are a type of machine learning that handles a wide range of Natural Language Processing (NLP) scenarios. Recently, in December 2022, a company...
https://link.springer.com/article/10.1007/s10489-024-05298-0
arxiv.org
https://arxiv.org/pdf/2506.08872v1
For the same paper, AI-assisted reviews gave an average score 0.14 points higher than human reviews.
arxiv.org
https://arxiv.org/pdf/2405.02150
Idiosyncrasies in Large Language Models
In this work, we unveil and study idiosyncrasies in Large Language Models (LLMs) -- unique patterns in their outputs that can be used to distinguish the models. To do so, we consider a simple...
https://arxiv.org/abs/2502.12150
People who frequently use ChatGPT for writing tasks are accurate...
In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either...
https://arxiv.org/abs/2501.15654