# The Widespread Adoption of Large Language Model-Assisted Writing Across Society

arXiv: https://arxiv.org/abs/2502.09747 · HTML: https://mlsanity.com/paper/2502.09747/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (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)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [The Rise of AI-Generated Content in Wikipedia](https://mlsanity.com/paper/2410.08044/) (2024)
- [Generative AI and Perceptual Harms: Who's Suspected of using LLMs?](https://mlsanity.com/paper/2410.00906/) (2024)
- [The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry](https://mlsanity.com/paper/2311.16466/) (2023)
- [Using ChatGPT for Thematic Analysis](https://mlsanity.com/paper/2405.08828/) (2024)

## Built on by (most cited first, 30 of 51)

- [The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models](https://mlsanity.com/paper/2502.11266/) (2025)
- [AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation](https://mlsanity.com/paper/2504.07532/) (2025)
- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models](https://mlsanity.com/paper/2505.20087/) (2025)
- [Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation](https://mlsanity.com/paper/2503.19092/) (2025)
- [Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach](https://mlsanity.com/paper/2509.22272/) (2025)
- [AI use in American newspapers is widespread, uneven, and rarely disclosed](https://mlsanity.com/paper/2510.18774/) (2025)
- [Can Good Writing Be Generative? Expert-Level AI Writing Emerges through Fine-Tuning on High-Quality Books](https://mlsanity.com/paper/2601.18353/) (2026)
- [Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs](https://mlsanity.com/paper/2512.03994/) (2025)
- [Political Ideology Shifts in Large Language Models](https://mlsanity.com/paper/2508.16013/) (2025)
- [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025)
- [The Echo Chamber Multi-Turn LLM Jailbreak](https://mlsanity.com/paper/2601.05742/) (2026)
- [On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?](https://mlsanity.com/paper/2510.20810/) (2025)
- [Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook](https://mlsanity.com/paper/2603.23279/) (2026)
- [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026)
- [How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experiments](https://mlsanity.com/paper/2508.09614/) (2025)
- [Cmprsr: Abstractive Token-Level Question-Agnostic Prompt Compressor](https://mlsanity.com/paper/2511.12281/) (2025)
- [The American Ghost in the Machine: How language models align culturally and the effects of cultural prompting](https://mlsanity.com/paper/2512.12488/) (2025)
- [The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding](https://mlsanity.com/paper/2601.15485/) (2026)
- [Can We Still Trace L1 Signals? Investigating the Resilience of Native Language Signals in the LLM Era](https://mlsanity.com/paper/2604.08568/) (2026)
- [Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style](https://mlsanity.com/paper/2604.24444/) (2026)
- [LLMs Generate Kitsch](https://mlsanity.com/paper/2604.25929/) (2026)
- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)
- [Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond](https://mlsanity.com/paper/2606.18062/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models](https://mlsanity.com/paper/2607.27512/) (2026)
- [Evaluating Large Language Model Performance on International Maritime Dangerous Goods Code Compliance](https://mlsanity.com/paper/2608.21036/) (2026)
- [MarkSec: Capability-Aware Evaluation of Adversarial Attacks Against LLM Watermarks](https://mlsanity.com/paper/2609.16681/) (2026)
- [Deconstructing Jazz Piano Style Using Machine Learning](https://mlsanity.com/paper/2504.05009/) (2025)
- [LongRecall: A Structured Approach for Robust Recall Evaluation in Long-Form Text](https://mlsanity.com/paper/2508.15085/) (2025)

## Abstract (arXiv)

The recent advances in large language models (LLMs) attracted significant public and policymaker interest in its adoption patterns. In this paper, we systematically analyze LLM-assisted writing across four domains-consumer complaints, corporate communications, job postings, and international organization press releases-from January 2022 to September 2024. Our dataset includes 687,241 consumer complaints, 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations (UN) press releases. Using a robust population-level statistical framework, we find that LLM usage surged following the release of ChatGPT in November 2022. By late 2024, roughly 18% of financial consumer complaint text appears to be LLM-assisted, with adoption patterns spread broadly across regions and slightly higher in urban areas. For corporate press releases, up to 24% of the text is attributable to LLMs. In job postings, LLM-assisted writing accounts for just below 10% in small firms, and is even more common among younger firms. UN press releases also reflect this trend, with nearly 14% of content being generated or modified by LLMs. Although adoption climbed rapidly post-ChatGPT, growth appears to have stabilized by 2024, reflecting either saturation in LLM adoption or increasing subtlety of more advanced models. Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications.