# Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English arXiv: https://arxiv.org/abs/2508.00238 ยท HTML: https://mlsanity.com/paper/2508.00238/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 39 of its 84 references that are in the map. - First posted on arXiv 1 Aug 2025. ## Builds on (most cited first, 30 of 39) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023) - [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017) - [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019) - [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016) - [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020) - [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022) - [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020) - [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019) - [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020) - [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023) - [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023) - [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023) - [Challenges in Detoxifying Language Models](https://mlsanity.com/paper/2109.07445/) (2021) - [Social Biases through the Text-to-Image Generation Lens](https://mlsanity.com/paper/2304.06034/) (2023) - [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024) - [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023) - [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023) - [Research Priorities for Robust and Beneficial Artificial Intelligence](https://mlsanity.com/paper/1602.03506/) (2016) - [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025) - [Will Large-scale Generative Models Corrupt Future Datasets?](https://mlsanity.com/paper/2211.08095/) (2022) - [Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval](https://mlsanity.com/paper/2407.20371/) (2024) - [From Lists to Emojis: How Format Bias Affects Model Alignment](https://mlsanity.com/paper/2409.11704/) (2024) - [Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges](https://mlsanity.com/paper/2408.08946/) (2024) - [Empirical evidence of Large Language Model's influence on human spoken communication](https://mlsanity.com/paper/2409.01754/) (2024) - [ChatGPT "contamination": estimating the prevalence of LLMs in the scholarly literature](https://mlsanity.com/paper/2403.16887/) (2024) - [Evaluating Biased Attitude Associations of Language Models in an Intersectional Context](https://mlsanity.com/paper/2307.03360/) (2023) - [The Impact of Large Language Models in Academia: from Writing to Speaking](https://mlsanity.com/paper/2409.13686/) (2024) - [Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles](https://mlsanity.com/paper/2308.04346/) (2023) - [Towards the relationship between AIGC in manuscript writing and author profiles: evidence from preprints in LLMs](https://mlsanity.com/paper/2404.15799/) (2024) ## Built on by (most cited first, 4 of 4) - [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026) - [Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning](https://mlsanity.com/paper/2606.00334/) (2026) - [Fully Automated Identification of Lexical Alignment and Preference-Stage Shifts in Large Language Models](https://mlsanity.com/paper/2606.03165/) (2026) - [Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognition](https://mlsanity.com/paper/2609.07474/) (2026) ## Abstract (arXiv) In recent years, written language, particularly in science and education, has undergone remarkable shifts in word usage. These changes are widely attributed to the growing influence of Large Language Models (LLMs), which frequently rely on a distinct lexical style. Divergences between model output and target audience norms can be viewed as a form of misalignment. While these shifts are often linked to using Artificial Intelligence (AI) directly as a tool to generate text, it remains unclear whether the changes reflect broader changes in the human language system itself. To explore this question, we constructed a dataset of 22.1 million words from unscripted spoken language drawn from conversational science and technology podcasts. We analyzed lexical trends before and after ChatGPT's release in 2022, focusing on commonly LLM-associated words. Our results show a moderate yet significant increase in the usage of these words post-2022, suggesting a convergence between human word choices and LLM-associated patterns. In contrast, baseline synonym words exhibit no significant directional shift. Given the short time frame and the number of words affected, this may indicate the onset of a remarkable shift in language use. Whether this represents natural language change or a novel shift driven by AI exposure remains an open question. Similarly, although the shifts may stem from broader adoption patterns, it may also be that upstream training misalignments ultimately contribute to changes in human language use. These findings parallel ethical concerns that misaligned models may shape social and moral beliefs.