Made-in China, Thinking in America:U.S. Values Persist in Chinese LLMs

• URL: http://arxiv.org/abs/2512.13723v1
• Date: Sat, 13 Dec 2025 02:52:57 GMT
• Title: Made-in China, Thinking in America:U.S. Values Persist in Chinese LLMs
• Authors: David Haslett, Linus Ta-Lun Huang, Leila Khalatbari, Janet Hui-wen Hsiao, Antoni B. Chan,
• Abstract summary: We conducted the first large-scale investigation of how models made in China and the USA align with people from China and the USA.<n>All models respond to both surveys more like American people than like Chinese people.<n>This skew toward American values is only slightly mitigated when prompting the models in Chinese or imposing a Chinese persona on the models.
• Score: 37.32224196180997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models increasingly mediate access to information and facilitate decision-making, they are becoming instruments in soft power competitions between global actors such as the United States and China. So far, language models seem to be aligned with the values of Western countries, but evidence for this ethical bias comes mostly from models made by American companies. The current crop of state-of-the-art models includes several made in China, so we conducted the first large-scale investigation of how models made in China and the USA align with people from China and the USA. We elicited responses to the Moral Foundations Questionnaire 2.0 and the World Values Survey from ten Chinese models and ten American models, and we compared their responses to responses from thousands of Chinese and American people. We found that all models respond to both surveys more like American people than like Chinese people. This skew toward American values is only slightly mitigated when prompting the models in Chinese or imposing a Chinese persona on the models. These findings have important implications for a near future in which large language models generate much of the content people consume and shape normative influence in geopolitics.

Related papers

• The performances of the Chinese and U.S. Large Language Models on the Topic of Chinese Culture [11.591457038182838]
  This study adopts a direct-questioning paradigm to evaluate models such as GPT-5.1, DeepSeek-V3.2, Qwen3-Max, and Gemini2.5Pro.<n>We assess their understanding of traditional Chinese culture, including history, literature, poetry, and related domains.
  arXiv  Detail & Related papers  (2026-01-06T09:03:01Z)
• Cross-Platform Short-Video Diplomacy: Topic and Sentiment Analysis of China-US Relations on Douyin and TikTok [53.79007551410356]
  We examine discussions surrounding China-U.S. relations on the Chinese and American social media platforms textitDouyin and textitTikTok.<n>This study analyzed 4,040 videos and 338,209 user comments to assess the public discussions and sentiments on social media regarding China-U.S. relations.
  arXiv  Detail & Related papers  (2025-10-25T19:28:58Z)
• A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies [7.778189232254147]
  This study evaluates the ability of an open-source large language model (LLM) to simulate public opinions in comparison to models developed by tech companies.<n>Our findings indicate that DeepSeek-V3 performs best in simulating U.S. opinions on the abortion issue.<n>For Chinese samples, DeepSeek-V3 performs best in simulating opinions on foreign aid and individualism but shows limitations in modeling views on capitalism.
  arXiv  Detail & Related papers  (2025-06-17T19:19:14Z)
• Do Chinese models speak Chinese languages? [3.1815791977708834]
  Language ability provides insights into pre-training data curation.<n>China has a long history of explicit language policy, varying between inclusivity of minority languages and a Mandarin-first policy.<n>We test performance of Chinese and Western open-source LLMs on Asian regional and Chinese minority languages.
  arXiv  Detail & Related papers  (2025-03-31T23:19:08Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Red AI? Inconsistent Responses from GPT3.5 Models on Political Issues in the US and China [13.583047010078648]
  This study investigates political biases in GPT's multilingual models. We posed the same question about political issues in the U.S. and China to GPT in both English and simplified Chinese. Our analysis of the bilingual responses revealed that GPT's bilingual models' political "knowledge" (content) and the political "attitude" (sentiment) are significantly more inconsistent on political issues in China.
  arXiv  Detail & Related papers  (2023-12-15T16:25:56Z)
• Towards Measuring the Representation of Subjective Global Opinions in Language Models [26.999751306332165]
  Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. We develop a quantitative framework to evaluate whose opinions model-generated responses are more similar to. We release our dataset for others to use and build on.
  arXiv  Detail & Related papers  (2023-06-28T17:31:53Z)
• CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models [52.25049362267279]
  We present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models. The testing instances in the dataset are automatically derived from 3K+ high-quality templates manually authored with stringent quality control. Extensive experiments demonstrate the effectiveness of the dataset in detecting model bias, with all 10 publicly available Chinese large language models exhibiting strong bias in certain categories.
  arXiv  Detail & Related papers  (2023-06-28T14:14:44Z)
• Speaking Multiple Languages Affects the Moral Bias of Language Models [70.94372902010232]
  Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. Do the models capture moral norms from English and impose them on other languages? Our experiments demonstrate that, indeed, PMLMs encode differing moral biases, but these do not necessarily correspond to cultural differences or commonalities in human opinions.
  arXiv  Detail & Related papers  (2022-11-14T20:08:54Z)
• Do Multilingual Language Models Capture Differing Moral Norms? [71.52261949766101]
  Massively multilingual sentence representations are trained on large corpora of uncurated data. This may cause the models to grasp cultural values including moral judgments from the high-resource languages. The lack of data in certain languages can also lead to developing random and thus potentially harmful beliefs.
  arXiv  Detail & Related papers  (2022-03-18T12:26:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.