# The Impact of Large Language Models in Academia: from Writing to Speaking

arXiv: https://arxiv.org/abs/2409.13686 · HTML: https://mlsanity.com/paper/2409.13686/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 50 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 30 of 34)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text](https://mlsanity.com/paper/2305.17359/) (2023)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://mlsanity.com/paper/2311.09807/) (2023)
- [Real or Fake? Learning to Discriminate Machine from Human Generated Text](https://mlsanity.com/paper/1906.03351/) (2019)
- [MGTBench: Benchmarking Machine-Generated Text Detection](https://mlsanity.com/paper/2303.14822/) (2023)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples](https://mlsanity.com/paper/2307.11729/) (2023)
- [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023)
- [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024)
- [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024)
- [LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?](https://mlsanity.com/paper/2401.05952/) (2024)
- [AI and the Problem of Knowledge Collapse](https://mlsanity.com/paper/2404.03502/) (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)
- [Is ChatGPT Transforming Academics' Writing Style?](https://mlsanity.com/paper/2404.08627/) (2024)
- [Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated](https://mlsanity.com/paper/2406.18259/) (2024)
- [Self-Cognition in Large Language Models: An Exploratory Study](https://mlsanity.com/paper/2407.01505/) (2024)

## Built on by (most cited first, 18 of 18)

- [Human-LLM Coevolution: Evidence from Academic Writing](https://mlsanity.com/paper/2502.09606/) (2025)
- [On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing](https://mlsanity.com/paper/2412.17242/) (2024)
- [Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models](https://mlsanity.com/paper/2412.11385/) (2024)
- [Large Language Models Penetration in Scholarly Writing and Peer Review](https://mlsanity.com/paper/2502.11193/) (2025)
- [Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning](https://mlsanity.com/paper/2510.19733/) (2025)
- [Wikipedia in the Era of LLMs: Evolution and Risks](https://mlsanity.com/paper/2503.02879/) (2025)
- [Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English](https://mlsanity.com/paper/2508.00238/) (2025)
- [Improving Math Problem Solving in Large Language Models Through Categorization and Strategy Tailoring](https://mlsanity.com/paper/2411.00042/) (2024)
- [NLLG Quarterly arXiv Report 09/24: What are the most influential current AI Papers?](https://mlsanity.com/paper/2412.12121/) (2024)
- [On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?](https://mlsanity.com/paper/2510.20810/) (2025)
- [Markovian Generation Chains in Large Language Models](https://mlsanity.com/paper/2603.11228/) (2026)
- [Word Overuse and Alignment in Large Language Models: The Influence of Learning from Human Feedback](https://mlsanity.com/paper/2508.01930/) (2025)
- [AI in the Writing Process: How Purposeful AI Support Fosters Student Writing](https://mlsanity.com/paper/2506.20595/) (2025)
- [Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI](https://mlsanity.com/paper/2412.01459/) (2024)
- [code_transformed: The Influence of Large Language Models on Code](https://mlsanity.com/paper/2506.12014/) (2025)
- [AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists](https://mlsanity.com/paper/2605.21481/) (2026)
- [Divination by Prompt: LLM-Mediated Xuanxue on Chinese Social Media](https://mlsanity.com/paper/2606.12418/) (2026)
- [Linguistic Monoculture in LLM-Assisted Language Use](https://mlsanity.com/paper/2607.27134/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine learning conferences, we examined and compared the words used in writing and speaking, representing the first large-scale study of how LLMs influence the two main modes of verbal communication and expression within the same group of people. Our empirical results show that LLM-style words such as "significant" have been used more frequently in abstracts and oral presentations. The impact on speaking is beginning to emerge and is likely to grow in the future, calling attention to the implicit influence and ripple effect of LLMs on human society.