中国AI模式政治复杂，不只是政府的口才，研究发现

中国AI模式政治复杂，不只是政府的口才，研究发现

Vals AI 显示的研究认为，中国AI模型对政治问题的反应不如预期统一。 西方模式也偶尔采取中国政府的官方立场。

中国AI模式在政治敏感问题上有牵引北京官方一行或完全拒绝回答的口碑。 AI 基准测试事务所Vals AI的研究描绘出更复杂的画面。 研究者们问了中国，美国等语言模型上百个关于中国的问题，在测试中改变了语言，用法背景，以及回复格式。

瓦尔斯称与中国政府立场的量度一致是"亲中国倾斜". 得分从−1到+1不等，其中正值表示对官方立场或亲政府立场的更一致，而负值则表示更具批判性的反应。

总的来说，中国模式的亲政府倾斜比预期的要低，根据瓦尔斯的说法，尽管个别模式之间存在重大差异。 例如，GLM-5.3 Flash明确证实，当问到中国侵犯人权的情况时，中国的侵犯人权情况已有大量记录。 反之，一些非华人模式偶尔会与中国政府在政治问题上的官方立场保持一致。

中文引发反政府反应

研究显示认为"即时"的语言对反应的政治倾斜有强烈的影响。 对于大多数测试过的模型来说，简化汉语的答案比英语，俄语或西班牙语的答案更亲政府。 这种效果在中美模式中都有所显现，而在中国模式中则更为明显。 瓦尔斯甚至发现在中国大陆使用的简化汉字与台湾和香港常见的传统汉字之间有较小但相通的相差。

瓦尔斯怀疑语言模型从语言本身推断出预期的视角。 另一个实验支持这个想法。 当一个系统提示称该模型正被中国大陆的一家公司内部使用时，一些模式的反应方式比该提示的美国使用方式更亲政府。

模式采取官方立场，而不是拒绝回答

常见的假设是：中国模型主要阻塞敏感问题，只在瓦尔斯测试中对某些DeepSeek模型有效。 其他大多数中国模特很少拒绝回答。 相反，他们有时采取官方的政府立场。 例如，Kimi K3在被问到新疆时否认有为Uyghurs设立的集中营，称这些设施是旨在打击极端主义和恐怖主义的职业培训中心。

所要求的反应格式也改变了结果。 当模型不得不用简单的是或否来回答政治问题时，GLM-5.3 Flash和MiMo V.2.6 Pro得分少得多亲政府。

嵌入到代理框架中的模型也改变了答案。 当瓦尔斯为编码助理提供中文模型系统说明和工具定义时，几个在亲政府尺度上得分较低。 模型很少使用工具。 当研究人员让模型访问网页搜索时，也出现了类似的效果。 尽管这些模型从未实际搜索过网络

即使在例行任务中，政府也会使用显示的用词

这些结论是否转化为日常用途仍然是一个有待解决的问题。 瓦尔斯认为，政治倾向在更现实的工作任务中比在有针对性的测试问题中更难发现。 公司仍然发现了个别的例子。

在将旅行应用程序的国家代码列表翻译为简化中文时，大多数被测试的模型翻译了"台湾"而没有任何限定词。 然而，Qwen3.8 Max使它成为"中国台湾"，这个标签暗示了台湾属于中国，被中国政府所使用。

OpenAI的图像模型 GPT 图像 2.5. Sunburst在所出版的地图实例Vals中也显示了语言依赖性的差异。 在中文促销后，将台湾标为"台湾行省"并添加了北京为标注南海地域声称而使用的有争议的九达线。 在所显示的英语结果中，没有这些要素。

目标微调改变模式行为。 Thomson 1.0 Small，由 Thomson路透社进行微调的模型，在测试中采用中国官方立场的频率远低于它原先基于的Qwen3.6 35B. 这一比例从84.6%下降到15.0%。 然而，一些个别的答案仍然捍卫了北京"一个中国"原则和同台湾"和平统一".

拒绝在测试分数中计为中性

瓦尔斯使用了政治测试问题，认为称公司并不代表典型的用户提示。 问题使用GPT-6 Astra并进行抽查翻译，而回答则由GPT-5.6 Luna自动打分。 拒绝默认为中性。 这意味着即使一个模式可以阻挡每一个问题，在尺度上也可以显得中立。

许多中国模式通过第三方供应商而不是制造商的官方服务进行测试。 官方出价可能应用额外的内容过滤器，因此结果并不一定会延续到公开的聊天员。

Aleph Alpha和"新闻卫视"先前的研究已经表明，中国AI模型经常采用关于敏感话题的官方叙事。 中国的条例也要求公开提供的AI服务与"核心社会主义价值观"保持一致。

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Chinese AI models are politically complex, not just government mouthpieces, study finds

A study by Vals AI shows that Chinese AI models respond to political questions less uniformly than expected. Western models, too, occasionally adopt official Chinese government positions.

Chinese AI models have a reputation for toeing Beijing's official line on politically sensitive questions or refusing to answer them at all. A study by AI benchmark firm Vals AI paints a more complex picture. The researchers asked Chinese, American, and other language models hundreds of questions about China, varying the language, usage context, and response format across tests.

Vals calls the measured alignment with Chinese government positions a "pro-China lean." Scores range from −1 to +1, where positive values indicate greater agreement with official positions or a pro-government stance, and negative values indicate more critical responses.

Overall, the pro-government lean of Chinese models was lower than expected, according to Vals, though there were major differences between individual models. GLM-5.3 Flash, for example, explicitly confirmed that China's human rights violations are well documented when asked about them. On the flip side, some non-Chinese models occasionally aligned with official Chinese government positions on political questions.

Responses shift pro-government when prompted in Chinese

The study shows that the language of the prompt has a strong effect on the political slant of responses. For most models tested, answers in Simplified Chinese were more pro-government than those in English, Russian, or Spanish. The effect showed up in both Chinese and American models but was far more pronounced in the Chinese ones. Vals even found a smaller but consistent gap between Simplified Chinese, which is used on mainland China, and Traditional Chinese, which is common in Taiwan and Hong Kong.

Vals suspects that language models infer the expected perspective from the language itself. Another experiment supports this idea. When a system prompt stated that the model was being used internally by a company on mainland China, some models responded in a more pro-government way than when the prompt indicated US-based usage.

Models adopt official positions instead of refusing to answer

The common assumption that Chinese models mainly block sensitive questions only held true for certain DeepSeek models in Vals' tests. Most other Chinese models rarely refused to answer. Instead, they sometimes adopted official government positions. Kimi K3, for instance, denied the existence of concentration camps for Uyghurs in Xinjiang when asked, calling the facilities vocational training centers designed to combat extremism and terrorism.

The required response format also changed results. When models had to answer political questions with a simple yes or no instead of formulating open-ended responses, GLM-5.3 Flash and MiMo V2.6 Pro scored much less pro-government.

Embedding models in an agentic framework changed answers too. When Vals gave Chinese models system instructions and tool definitions for coding assistants, several scored lower on the pro-government scale. The models rarely used the tools. A similar effect showed up when researchers gave models access to web search. Responses became less pro-government even though the models never actually searched the web.

Government phrasing shows up even in routine tasks

Whether these findings translate to everyday use remains an open question. According to Vals, political tendencies were much harder to detect in more realistic work tasks than in targeted test questions. The company still found individual examples, though.

When translating a list of country codes for a travel app into Simplified Chinese, most models tested translated "Taiwan" without any qualifier. Qwen3.8 Max, however, rendered it as "Taiwan, China," a label that implies Taiwan belongs to China and is used by the Chinese government.

OpenAI's image model GPT Image 2.5 Sunburst also showed language-dependent differences in the map examples Vals published. When prompted in Chinese, it labeled Taiwan as "Taiwan Province" and added the disputed nine-dash line that Beijing uses to mark territorial claims in the South China Sea. In the English-language result shown, these elements were absent.

Targeted fine-tuning changed model behavior. Thomson 1.0 Small, a model fine-tuned by Thomson Reuters, adopted official Chinese positions far less often in testing than the original Qwen3.6 35B it was based on. The share dropped from 84.6 to 15.0 percent. Some individual answers, however, still defended Beijing's One-China principle and the "peaceful reunification" with Taiwan.

Refusals count as neutral in the test scoring

Vals used political test questions that the company says aren't representative of typical user prompts. The questions were translated using GPT-6 Astra and spot-checked, while responses were scored automatically by GPT-5.6 Luna. Refusals counted as neutral by default. That means even a model that blocks every single question could appear neutral on the scale.

Many Chinese models were tested through third-party providers rather than the manufacturers' official services. The official offerings may apply additional content filters, so the results don't necessarily carry over to the publicly available chatbots.

Earlier studies by Aleph Alpha and NewsGuard had already shown that Chinese AI models frequently adopt official narratives on sensitive topics. China's regulations also require publicly offered AI services to align with "core socialist values."

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