Millions of people around the world query large language models (LLMs) for information. Although several studies have compellingly documented the persuasive potential of these models[1-10], there is limited evidence of who or what influences the models themselves, leading to a flurry of concerns about which companies and governments build and regulate the models. Here we show through six studies that government control of the media across the world already influences the output of LLMs via their training data. We use a cross-national audit to show that LLMs exhibit a stronger pro-government valence in the languages of countries with lower media freedom than in those with higher media freedom. This result is correlational, so to triangulate the specific mechanism of how state media control can influence LLMs, we develop a multi-part case study on China's media. We demonstrate that media scripted and curated by the Chinese state appears in LLM training datasets. To evaluate the plausible effect of this inclusion, we use an open-weight model to show that additional pretraining on Chinese state-coordinated media generates more positive answers to prompts about Chinese political institutions and leaders. We link this phenomenon to commercial models through two audit studies demonstrating that prompting models in Chinese generates more positive responses about China's institutions and leaders than do the same queries in English. The combination of influence and persuasive potential across languages suggests the troubling conclusion that states and powerful institutions have increased strategic incentives to leverage media control in the hopes of shaping LLM output.
Abstract
Journal Article
eng
42129566
Waight, Hannah, et al. "State Media Control Influences Large Language Models." Nature, 2026.
Waight H, Yang E, Yuan Y, et al. State media control influences large language models. Nature. 2026.
Waight, H., Yang, E., Yuan, Y., Messing, S., Roberts, M. E., Stewart, B. M., & Tucker, J. A. (2026). State media control influences large language models. Nature. https://doi.org/10.1038/s41586-026-10506-7
Waight H, et al. State Media Control Influences Large Language Models. Nature. 2026 May 13; PubMed PMID: 42129566.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR
T1 - State media control influences large language models.
AU - Waight,Hannah,
AU - Yang,Eddie,
AU - Yuan,Yin,
AU - Messing,Solomon,
AU - Roberts,Margaret E,
AU - Stewart,Brandon M,
AU - Tucker,Joshua A,
Y1 - 2026/05/13/
PY - 2024/10/25/received
PY - 2026/04/08/accepted
PY - 2026/5/14/medline
PY - 2026/5/14/pubmed
PY - 2026/5/13/entrez
JF - Nature
JO - Nature
N2 - Millions of people around the world query large language models (LLMs) for information. Although several studies have compellingly documented the persuasive potential of these models[1-10], there is limited evidence of who or what influences the models themselves, leading to a flurry of concerns about which companies and governments build and regulate the models. Here we show through six studies that government control of the media across the world already influences the output of LLMs via their training data. We use a cross-national audit to show that LLMs exhibit a stronger pro-government valence in the languages of countries with lower media freedom than in those with higher media freedom. This result is correlational, so to triangulate the specific mechanism of how state media control can influence LLMs, we develop a multi-part case study on China's media. We demonstrate that media scripted and curated by the Chinese state appears in LLM training datasets. To evaluate the plausible effect of this inclusion, we use an open-weight model to show that additional pretraining on Chinese state-coordinated media generates more positive answers to prompts about Chinese political institutions and leaders. We link this phenomenon to commercial models through two audit studies demonstrating that prompting models in Chinese generates more positive responses about China's institutions and leaders than do the same queries in English. The combination of influence and persuasive potential across languages suggests the troubling conclusion that states and powerful institutions have increased strategic incentives to leverage media control in the hopes of shaping LLM output.
SN - 1476-4687
UR - https://www.unboundmedicine.com/prime/citation/42129566/State_media_control_influences_large_language_models.
DB - PRIME
DP - Unbound Medicine
ER -
