# Empirical evidence of Large Language Model's influence on human spoken communication

arXiv: https://arxiv.org/abs/2409.01754 · HTML: https://mlsanity.com/paper/2409.01754/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 84 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Powerset multi-class cross entropy loss for neural speaker diarization](https://mlsanity.com/paper/2310.13025/) (2023)
- [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://mlsanity.com/paper/2311.09807/) (2023)
- [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [Diminished Diversity-of-Thought in a Standard Large Language Model](https://mlsanity.com/paper/2302.07267/) (2023)
- [Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty](https://mlsanity.com/paper/2303.07462/) (2023)
- [Is ChatGPT Transforming Academics' Writing Style?](https://mlsanity.com/paper/2404.08627/) (2024)
- [Synthetic Controls in Action](https://mlsanity.com/paper/2203.06279/) (2022)
- [How the medium shapes the message: Printing and the rise of the arts and sciences](https://mlsanity.com/paper/1512.05020/) (2015)

## Built on by (most cited first, 27 of 27)

- [The Impact of Large Language Models in Academia: from Writing to Speaking](https://mlsanity.com/paper/2409.13686/) (2024)
- [The Lock-in Hypothesis: Stagnation by Algorithm](https://mlsanity.com/paper/2506.06166/) (2025)
- [Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models](https://mlsanity.com/paper/2412.11385/) (2024)
- [Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English](https://mlsanity.com/paper/2508.00238/) (2025)
- [Recognising, Anticipating, and Mitigating LLM Pollution of Online Behavioural Research](https://mlsanity.com/paper/2508.01390/) (2025)
- [NLLG Quarterly arXiv Report 09/24: What are the most influential current AI Papers?](https://mlsanity.com/paper/2412.12121/) (2024)
- [Will AI shape the way we speak? The emerging sociolinguistic influence of synthetic voices](https://mlsanity.com/paper/2504.10650/) (2025)
- [On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?](https://mlsanity.com/paper/2510.20810/) (2025)
- [Architecting Trust in Artificial Epistemic Agents](https://mlsanity.com/paper/2603.02960/) (2026)
- [Markovian Generation Chains in Large Language Models](https://mlsanity.com/paper/2603.11228/) (2026)
- [AI Can Enhance Creativity in Social Networks](https://mlsanity.com/paper/2410.15264/) (2024)
- [Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback](https://mlsanity.com/paper/2508.01930/) (2025)
- [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026)
- [Arti-"fickle" Intelligence: Using LLMs as a Tool for Inference in the Political and Social Sciences](https://mlsanity.com/paper/2504.03822/) (2025)
- [AI-Mediated Communication Can Steer Collective Opinion](https://mlsanity.com/paper/2605.16245/) (2026)
- [Solipsistic Superintelligence is Unlikely to be Cooperative](https://mlsanity.com/paper/2606.03237/) (2026)
- [Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI](https://mlsanity.com/paper/2506.15468/) (2025)
- [Pragmatics beyond humans: meaning, communication, and LLMs](https://mlsanity.com/paper/2508.06167/) (2025)
- [Artificially intelligent agents in the social and behavioral sciences: A history and outlook](https://mlsanity.com/paper/2510.05743/) (2025)
- [Voice-based debate with an AI adversary is associated with increased divergent ideation](https://mlsanity.com/paper/2603.27073/) (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)
- [Understanding Large Language Models](https://mlsanity.com/paper/2607.01006/) (2026)
- [An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-2026](https://mlsanity.com/paper/2609.07447/) (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)
- [Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems](https://mlsanity.com/paper/2610.01257/) (2026)
- [Gacha Decoding: Eliciting Diverse Generations Through Instruction Following](https://mlsanity.com/paper/2610.01382/) (2026)

## Abstract (arXiv)

From the printing press to social media, innovations in communication technology have repeatedly reshaped how ideas spread through human culture. Chatbots powered by generative artificial intelligence constitute a new medium, encoding cultural patterns in their neural representations and disseminating them in conversations with hundreds of millions of people. Whether these patterns transmit into human language, and ultimately shape human culture, is a fundamental question. While fully quantifying the causal impact of a chatbot like ChatGPT on human culture is challenging, lexical shifts in human spoken communication may offer an early indicator. Here we show that words preferentially generated by ChatGPT, such as delve, showcase, boast, intricacies and meticulous, increased abruptly in spontaneous human speech. A synthetic-control analysis of 737,083 hours of conversation from 824,634 podcast episodes, screened for unscripted speech, causally links this shift to ChatGPT's release. The measurable influence on spontaneous speech suggests that humans internalize the lexical choices of large language models (LLMs). A preregistered experiment (N = 496) confirms they do, as a brief chatbot interaction led participants to adopt its words as their own, persisting past a distractor task and confirmed in forced lexical choice, indicating entrenchment in the active vocabulary. Together these results show that machines trained on human data now feed their own traits back into human language, integrating LLMs into the ongoing processes of cultural evolution.. This coupling raises concerns about linguistic homogenization and the capacity of a few major AI providers for latent cultural influence at scale.