基于LLM的舆论模拟的跨文化比较：在中美多元社会中对中美模型的评估
A Cross-Cultural Comparison of LLM-based Public Opinion Simulation: Evaluating Chinese and U.S. Models on Diverse Societies
AI总结：
本研究评估了开源LLM DeepSeek模拟中美公众舆论的能力，发现DeepSeek-V3在模拟美国堕胎议题及中国对外援助和个人主义议题上表现最佳，但在资本主义观点模拟上存在局限，并揭示了LLM普遍存在群体内过度概括单一视角的偏差。
摘要：
本研究评估了开源大语言模型（LLM）DeepSeek模拟公众舆论的能力，并将其与大型科技公司开发的LLM进行了比较。通过将DeepSeek-R1和DeepSeek-V3与Qwen2.5、GPT-4o和Llama-3.3进行对比，并利用American National Election Studies (ANES)和中国Zuobiao数据集的调查数据，我们评估了这些模型预测中美两国社会议题公众舆论的能力，突出了它们在跨国比较中的能力。研究结果表明，在模拟美国关于堕胎问题的舆论时，DeepSeek-V3的表现优于气候变化、枪支管控、移民和同性伴侣服务等其他议题，主要因为它在提供民主党或自由派角色设定时能更准确地模拟回应。对于中国样本，DeepSeek-V3在模拟对外援助和个人主义的舆论时表现最佳，但在模拟资本主义观点方面存在局限性，特别是未能捕捉低收入和非大学教育人群的立场。在模拟传统主义和自由市场的舆论方面，它与其他模型没有显著差异。进一步分析表明，所有LLM都倾向于在人口统计群体内过度概括单一观点，通常默认在群体内给出一致的回应。这些发现凸显了在LLM驱动的舆论建模中缓解文化和人口偏差的必要性，呼吁采用更具包容性的训练方法等方法。
英文摘要：
This study evaluates the ability of DeepSeek, an open-source large language model (LLM), to simulate public opinions in comparison to LLMs developed by major tech companies. By comparing DeepSeek-R1 and DeepSeek-V3 with Qwen2.5, GPT-4o, and Llama-3.3 and utilizing survey data from the American National Election Studies (ANES) and the Zuobiao dataset of China, we assess these models' capacity to predict public opinions on social issues in both China and the United States, highlighting their comparative capabilities between countries. Our findings indicate that DeepSeek-V3 performs best in simulating U.S. opinions on the abortion issue compared to other topics such as climate change, gun control, immigration, and services for same-sex couples, primarily because it more accurately simulates responses when provided with Democratic or liberal personas. For Chinese samples, DeepSeek-V3 performs best in simulating opinions on foreign aid and individualism but shows limitations in modeling views on capitalism, particularly failing to capture the stances of low-income and non-college-educated individuals. It does not exhibit significant differences from other models in simulating opinions on traditionalism and the free market. Further analysis reveals that all LLMs exhibit the tendency to overgeneralize a single perspective within demographic groups, often defaulting to consistent responses within groups. These findings highlight the need to mitigate cultural and demographic biases in LLM-driven public opinion modeling, calling for approaches such as more inclusive training methodologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
