State media control influences large language models | Nature

• 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, 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.

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