On the Alignment of Large Language Models with Global Human Opinion

On the Alignment of Large Language Models with Global Human Opinion

大语言模型与全球人类意见的对齐

Yang Liu Masahiro Kaneko Chenhui Chu

Abstract

摘要

Today’s large language models (LLMs) are capable of supporting multilingual scenarios, allowing users to interact with LLMs in their native languages. When LLMs respond to subjective questions posed by users, they are expected to align with the views of specific demographic groups or historical periods, shaped by the language in which the user interacts with the model. Existing studies mainly focus on researching the opinions represented by LLMs among demographic groups in the United States or a few countries, lacking worldwide country samples and studies on human opinions in different historical periods, as well as lacking discussion on using language to steer LLMs. Moreover, they also overlook the potential influence of prompt language on the alignment of LLMs’ opinions. In this study, our goal is to fill these gaps. To this end, we create an evaluation framework based on the World Values Survey (WVS) to systematically assess the alignment of LLMs with human opinions across different countries, languages, and historical periods around the world. We find that LLMs appropriately or over-align the opinions with only a few countries while under-aligning the opinions with most countries. Furthermore, changing the language of the prompt to match the language used in the questionnaire can effectively steer LLMs to align with the opinions of the corresponding country more effectively than existing steering methods. At the same time, LLMs are more aligned with the opinions of the contemporary population. To our knowledge, our study is the first comprehensive investigation of the topic of opinion alignment in LLMs across global, language, and temporal dimensions. Our code and data are publicly available at https://github.com/ku-nlp/global-opinion-alignment and https://github.com/nlply/global-opinion-alignment .

今天的大语言模型（LLM）已经能够支持多语言场景，使用户可以用自己的母语与模型交互。当模型回答用户提出的主观问题时，人们期望它的立场能够与特定人口群体或历史时期的看法对齐，并受到用户与模型交互时所使用语言的影响。已有研究主要关注模型在美国或少数几个国家人口群体中所代表的意见，缺少覆盖全球各国的样本，也缺少对不同历史时期人类意见的研究，更缺少关于用语言来引导模型的讨论。此外，这些工作还忽视了提示语言本身可能对模型意见对齐产生的影响。本研究旨在填补这些空白。为此，我们基于世界价值观调查（World Values Survey，WVS）构建了一套评测框架，系统评估大语言模型在全球不同国家、语言和历史时期上与人类意见的对齐情况。我们发现，模型只与少数国家的意见对齐得当或过度对齐，而对大多数国家则对准不足。此外，把提示语言改成与问卷所用语言一致，能够比现有引导方法更有效地引导模型与相应国家的意见对齐。与此同时，模型更接近当代人群的意见。据我们所知，本研究是首次从全球、语言和时间三个维度，对大语言模型意见对齐问题进行的全面考察。我们的代码和数据公开于 https://github.com/ku-nlp/global-opinion-alignment 和 https://github.com/nlply/global-opinion-alignment。

1Kyoto University

2MBZUAI

yangliu@nlp.ist.i.kyoto-u.ac.jp, Masahiro.Kaneko@mbzuai.ac.ae, chu@i.kyoto-u.ac.jp

1 Introduction

1 引言

Large language models (LLMs) have become crucial to everyday decision-making and assistance (OpenAI 2023a; Bubeck et al. 2023; Bommasani et al. 2021). Once LLMs are deployed as products, they are inevitably asked to answer subjective questions, not just objective ones (Ouyang et al. 2022; Santurkar et al. 2023; Meister et al. 2025). From an alignment perspective, next-token prediction during pretraining gives LLMs a statistical prior over human opinions (Radford et al. 2019; Brown et al. 2020), while subsequent alignment stages, such as instruction tuning (Wei et al. 2021; Sanh et al. 2021) and preference optimization (Christiano et al. 2017; Ouyang et al. 2022), shape this prior toward the opinions in their preference datasets, which are typically annotated by annotators from various countries, speaking diverse languages, and across different ages (Mason and Suri 2012).

大语言模型（LLM）已经成为日常决策与辅助中的关键工具 (OpenAI 2023a; Bubeck et al. 2023; Bommasani et al. 2021)。一旦模型作为产品上线，它们就不可避免地会被要求回答主观问题，而不仅仅是客观问题 (Ouyang et al. 2022; Santurkar et al. 2023; Meister et al. 2025)。从对齐的角度看，预训练中的下一词预测给模型提供了关于人类意见的统计先验 (Radford et al. 2019; Brown et al. 2020)；随后的对齐阶段，例如指令微调 (Wei et al. 2021; Sanh et al. 2021) 和偏好优化 (Christiano et al. 2017; Ouyang et al. 2022)，则把这一先验推向偏好数据集中的意见。这些数据通常由来自不同国家、使用不同语言、处于不同年龄段的标注者标注 (Mason and Suri 2012)。

Figure 1: An example of our main idea. When asking the LLM a subjective question, is the LLM’s opinion distribution closer to specific countries’ opinion distributions?

图 1：我们主要想法的一个示例。当向大语言模型提出一个主观问题时，模型的意见分布是否更接近某些特定国家的意见分布？

Existing study mainly focuses on exploring opinion alignment between LLMs and demographic groups in the United States (Santurkar et al. 2023). Durmus et al. (2023) expand Santurkar et al. (2023)’s study to explore the alignment between the LLM Claude and other countries, and they further find that translating the questions into Russian, Chinese, and Turkish fails to improve the alignment between the LLM and the language speakers. A crucial challenge to previous studies is that the LLMs’ opinion distribution is expressed as the next token log probabilities from the model, which suffers from a mis-calibration issue and requires the LLMs to be able to output logits (Meister et al. 2025). Therefore, in this study, we adopt the verbalized distribution method to investigate the alignment between LLMs and human opinions. We expand to investigate seven state-of-the-art LLMs and eight languages covering 10 countries to validate the effectiveness of language steering. In addition, we innovatively explore the alignment of LLMs and human opinions in the temporal dimension. Therefore, our study covers the alignment of LLMs with human opinions in three dimensions: global, languages, and temporal .

已有研究主要关注大语言模型与美国人口群体之间的意见对齐 (Santurkar et al. 2023)。Durmus et al. (2023) 把 Santurkar et al. (2023) 的研究扩展到考察 Claude 与其他国家的对齐，并进一步发现，把问题翻译成俄语、中文和土耳其语，并不能改善模型与这些语言使用者之间的对齐。先前研究面临的一个关键困难是，模型的意见分布被表示成下一词的对数概率，这既存在校准不良的问题，也要求模型能够输出 logits (Meister et al. 2025)。因此，本研究采用口头化分布（verbalized distribution）方法来考察模型与人类意见的对齐。我们把考察范围扩大到七个先进的大语言模型，以及覆盖 10 个国家的八种语言，以验证语言引导的有效性。此外，我们还首次探索了模型与人类意见在时间维度上的对齐。因此，本研究覆盖了模型与人类意见在三个维度上的对齐： 全球、语言和时间 。

The key question we focus on is illustrated in Figure 1: when asking the LLMs a subjective question, is the LLM’s opinion distribution closer to specific countries’ opinion distributions? If an LLM systematically reflects the opinions of specific countries, this may lead to unexpected country dominance or misalignment in international applications (Bender et al. 2021; AlKhamissi et al. 2024; Sukiennik et al. 2025). Additionally, analyzing the opinions reflected by the LLMs can help improve the transparency and accountability of the LLMs, allowing developers to better assess and mitigate potential geopolitical or ethical risks. Finally, this study can provide a foundation for building more human opinion adaptive or neutral AI systems that respect diverse views in the globally interconnected world.

我们关注的核心问题如图 1 所示：当向大语言模型提出一个主观问题时，模型的意见分布是否更接近某些特定国家的意见分布？如果一个模型系统性地反映某些特定国家的意见，就可能在国际应用中带来意想不到的国家主导或错位 (Bender et al. 2021; AlKhamissi et al. 2024; Sukiennik et al. 2025)。此外，分析模型所反映的意见，有助于提高模型的透明度和可问责性，使开发者能够更好地评估并缓解潜在的地缘政治或伦理风险。最后，这项研究也可以为构建更能适应人类意见、或更保持中立、并尊重全球互联世界中多样观点的人工智能系统提供基础。

The World Values Survey (Haerpfer et al. 2022, WVS;), a comprehensive global research program, systematically collects human opinions on a wide range of topics, including politics, religion, and ethics. The standardized questionnaire design of the WVS allows for consistent comparisons of opinions across countries, languages, and historical periods. In addition, the survey covers highly subjective and value-oriented questions, which align well with the types of prompts used to probe LLMs’ opinions. By using WVS as the benchmark, in this study, we focus on the following research questions: RQ1 : Do LLMs appropriately align with the opinions of different countries? RQ2 : Can language steer LLMs to express opinions closer to language speakers? RQ3 : Which human historical period’s opinions are reflected in LLMs’ responses?

世界价值观调查 (Haerpfer et al. 2022, WVS;) 是一项综合性的全球研究计划，系统收集人们在政治、宗教和伦理等广泛议题上的意见。WVS 标准化的问卷设计，使得跨国家、跨语言、跨历史时期的意见比较可以保持一致。此外，调查覆盖高度主观、以价值观为导向的问题，这与用来探测模型意见的提示类型十分契合。以 WVS 为基准，本研究关注以下研究问题： RQ1 ：大语言模型是否与不同国家的意见对齐得当？ RQ2 ：语言能否引导模型表达更接近该语言使用者的意见？ RQ3 ：模型的回答反映的是哪一历史时期的人类意见？

\times

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2 The World Values Survey

2 世界价值观调查

In this section, we explain why WVS is suitable for our study from three dimensions: global, language, and temporal. Further details are provided in Appendix A.

本节从全球、语言和时间三个维度说明，为什么 WVS 适合我们的研究。更多细节见附录 A。

Global Dimension The WVS covers highly subjective and value-oriented questions, which align well with the types of prompts used to probe LLMs’ opinions. The latest wave covers survey data from 66 countries and regions, enabling us to compare the alignment of LLMs with different countries’ human opinions. In addition, each country has more than 1,000 human questionnaire results, which ensures that the survey data reflects real human behavior to some extent. These features make it possible for us to study RQ1.

全球维度 WVS 覆盖高度主观、以价值观为导向的问题，这与用来探测模型意见的提示类型十分契合。最新一轮调查覆盖 66 个国家和地区的数据，使我们能够比较模型与不同国家人类意见的对齐情况。此外，每个国家都有超过 1,000 份人类问卷结果，这在一定程度上保证调查数据能够反映真实的人类行为。这些特征使我们能够研究 RQ1。

Language Dimension The WVS questionnaire was translated into all languages which serve as the first language for 15% (or more) of the population. For countries that participated in previous waves of the WVS, translation for the replicated items is used from the previous wave questionnaires to minimize bias during overtime comparison of answer distributions.[^2] According to our statistics, the latest wave provided more than 50 language translations, ensuring that the questionnaire covers languages supported by LLMs and providing linguistic diversity for RQ2.

语言维度 WVS 问卷被翻译成所有作为 15%（或以上）人口第一语言的语言。对于参加过往轮次 WVS 的国家，重复出现的题目沿用上一轮问卷中的译文，以尽量减少跨时间比较答案分布时的偏差。[^2] 据我们统计，最新一轮提供了超过 50 种语言的译文，这保证问卷覆盖了模型所支持的语言，并为 RQ2 提供了语言多样性。

Temporal Dimension Since 1981, the WVS has conducted seven waves of globally representative surveys. The WVS maintains functional consistency across most questions in all waves, allowing us to study long-term trends of alignment between LLMs and human opinions across different waves, providing temporal data support for our RQ3.

时间维度 自 1981 年以来，WVS 已经开展了七轮具有全球代表性的调查。WVS 在各轮次的大多数问题上保持功能上的一致，使我们能够研究模型与人类意见在不同轮次上的长期对齐趋势，从而为 RQ3 提供时间维度的数据支持。

3 Experiment Settings

3 实验设置

In this section, we introduce the experiment settings. More details such as the question filter rules and the complete country list (§3.1), the full prompts (§3.3), and more details of the distribution expression methods (§3.4) are provided in Appendix B.

本节介绍实验设置。题目筛选规则和完整国家列表（§3.1）、完整提示（§3.3），以及意见分布表达方法的更多细节（§3.4），见附录 B。

3.1 Dataset

3.1 数据集

In RQ1, we conduct a question filter process on the English version of wave 7, which includes survey data from 66 countries, to filter out questions that are not suitable for this study. For example, questions that require actual experience, objectivity, etc. After this filter process, we get 144 opinion-related questions. In RQ2, we used the same 144 questions and collected them from other language versions of the questionnaire. In RQ3, to ensure a certain number of common questions, we chose to analyze WVS’s data from wave 5, wave 6, and wave 7.[^3] Each wave corresponds to a historical questionnaire version. We organized each English version from wave 5 to 7 and then selected questions that are common in all three waves. We obtained 75 opinion-related questions that are shared across all three waves.

在 RQ1 中，我们在第 7 波英文版问卷上做题目筛选，该轮包含 66 个国家的调查数据，以滤除不适合本研究的题目，例如需要实际生活经验、属于客观题的题目等。筛选之后，我们得到 144 道与意见相关的题目。在 RQ2 中，我们使用同样的 144 道题，并从其他语言版本的问卷中收集对应题目。在 RQ3 中，为了保证有足够数量的共同题目，我们选择分析 WVS 第 5、第 6 和第 7 波的数据。[^3] 每一波对应一个历史问卷版本。我们整理了第 5 至第 7 波的各英文版本，再选出三轮中都出现的题目，得到 75 道在三轮中共有的意见相关题目。

3.2 Models

3.2 模型

We experiment with seven instruction-tuned multilingual LLMs: Aya-23-35B (Aryabumi et al. 2024), Llama3-70B-Instruct (AI@Meta 2024), Qwen2.5-72B-Instruct (Team 2024), GPT-3.5-Turbo (OpenAI 2023b, gpt-3.5-turbo-0125;), GPT-4 (Achiam et al. 2023, gpt-4-0613;), GPT-5 (OpenAI 2025, gpt-5;), DeepSeek-V3 (Liu et al. 2024, DeepSeek-V3-0324;), and DeepSeek-R1 (Guo et al. 2025, DeepSeek-R1-0528;). For open-weight community models, we select the largest publicly released weights in each series. For commercial LLMs, we include OpenAI’s GPT-3.5-Turbo, GPT-4, and GPT-5 (closed-source models), as well as DeepSeek-V3 and DeepSeek-R1, noting that DeepSeek releases open-weight models under a permissive license.

我们在七个经过指令微调的多语言大语言模型上做实验：Aya-23-35B (Aryabumi et al. 2024)、Llama3-70B-Instruct (AI@Meta 2024)、Qwen2.5-72B-Instruct (Team 2024)、GPT-3.5-Turbo (OpenAI 2023b, gpt-3.5-turbo-0125;)、GPT-4 (Achiam et al. 2023, gpt-4-0613;)、GPT-5 (OpenAI 2025, gpt-5;)、DeepSeek-V3 (Liu et al. 2024, DeepSeek-V3-0324;) 以及 DeepSeek-R1 (Guo et al. 2025, DeepSeek-R1-0528;)。对开放权重的社区模型，我们在每个系列中选择公开发布的最大权重。对商业模型，我们纳入 OpenAI 的 GPT-3.5-Turbo、GPT-4 和 GPT-5（闭源模型），以及 DeepSeek-V3 和 DeepSeek-R1，并注明 DeepSeek 以宽松许可发布开放权重模型。

3.3 Prompts

3.3 提示

As the instruction fine-tuned LLMs use “task instructions + examples” as input during training, it is easier for them to follow the requirements of the prompt and output content in the required format. We refer to Meister et al. (2025) to control the LLMs’ output distribution in JSON format (e.g., {1: 31%, 2: 4%, 3: 30%, 4: 35%}) by providing five few-shot examples.

由于经过指令微调的模型在训练时以“任务指令 + 示例”作为输入，它们更容易遵循提示要求，并按指定格式输出内容。我们参考 Meister et al. (2025)，通过提供五个少样本示例，把模型的输出分布控制为 JSON 格式（例如 {1: 31%, 2: 4%, 3: 30%, 4: 35%}）。

3.4 Distribution Expression Methods

3.4 分布表达方法

D_{\mathcal{M}}(q)

\mathcal{M}

c\in\mathcal{C}

c\in\mathcal{C}

D_{c,n}=\frac{|\mathcal{P}^{(n)}_{c}|}{\sum_{i\in\mathcal{N}}|\mathcal{P}^{(i)}_{c}|} \tag{1}

|\mathcal{P}_{c}^{(n)}|

|\mathcal{P}_{c}^{(n)}|

3.5 Alignment Metrics

3.5 对齐指标

D_{\mathcal{M}}(q)

D_{\mathcal{M}}(q)

\mathcal{A}(D_{\mathcal{M}},D_{c};\mathcal{Q})=\frac{1}{|\mathcal{Q}|}\sum_{q\in\mathcal{Q}}\left(1-\frac{\mathrm{WD}(D_{\mathcal{M}}(q),D_{c}(q))}{|\mathcal{N}|-1}\right) \tag{2}

\mathcal{Q}

\mathcal{Q}

4 Global Opinion Alignment (RQ1)

4 全球意见对齐（RQ1）

p

p

In this section, we calculate the alignment score between LLMs’ opinion distributions and human opinion distributions in wave 7. Then, we discuss the alignment of LLMs with human opinion at three levels: 1) Model level : Which LLM most matches the human opinion distribution? 2) Country level : Which countries’ opinions are aligned by LLMs? 3) Alignment difference level : If we take the average human opinion distribution as a possible goal, how do LLMs and this average distribution differ in terms of alignment with a country?

本节计算模型意见分布与第 7 波人类意见分布之间的对齐分数，再从三个层面讨论模型与人类意见的对齐： 1) 模型层面 ：哪一个模型与人类意见分布最为吻合？ 2) 国家层面 ：模型与哪些国家的意见更为对齐？ 3) 对齐差异层面 ：如果把人类平均意见分布当作一个可能的目标，模型与这一平均分布在对某一国家的对齐上有何差别？

4.1 Model Level

4.1 模型层面

As shown in Figure 2(a), we demonstrate the overall alignment scores of LLMs’ opinion distributions with human opinion distributions. The experimental results show that GPT-5 achieves the highest average alignment score (0.8621). Meanwhile, the average alignment scores of Llama3, Qwen2.5, GPT-4, DeepSeek-V3, and DeepSeek-R1 closely follow those of GPT-5, also demonstrating high human opinion alignment performance. In contrast, Aya-23-35B and GPT-3.5-Turbo, as earlier generations of instruction fine-tuning LLMs, have lower average alignment scores and a number of countries with low tail scores; the relative lack of multilingual long-tail coverage may be part of the reason. In addition, the alignment scores of each LLM differ significantly across countries, indicating that no single LLM can simultaneously align with all countries’ opinions.

如图 2(a) 所示，我们展示了模型意见分布与人类意见分布的总体对齐分数。实验结果表明，GPT-5 取得了最高的平均对齐分数（0.8621）。与此同时，Llama3、Qwen2.5、GPT-4、DeepSeek-V3 和 DeepSeek-R1 的平均对齐分数紧随 GPT-5 之后，同样表现出较高的人类意见对齐水平。相比之下，Aya-23-35B 和 GPT-3.5-Turbo 作为较早一代的指令微调模型，平均对齐分数更低，也有若干国家落在低分尾部；多语言长尾覆盖相对不足，可能是原因之一。此外，每个模型在不同国家上的对齐分数差异显著，说明没有任何一个模型能够同时与所有国家的意见对齐。

4.2 Country Level

4.2 国家层面

Next, we move our attention to the question of which countries LLMs are more aligned with . Figure 2(b) shows the five countries’ opinion distributions that are most and least aligned with DeepSeek-R1’ opinion distributions.[^5] We can see that DeepSeek-R1 has the highest average alignment scores with countries such as the United States and Chile (0.8972 and 0.8948), and relatively low scores with countries such as Egypt and Myanmar (0.7855 and 0.7881). Notably, the standard deviation of the alignment scores in the high-alignment countries is significantly lower than that in the low-alignment countries, indicating that DeepSeek-R1 is both approximate and robust in the high-alignment countries. In contrast, low-alignment countries suffer from both systematic misalignment and high standard deviations. This may be due to the widespread presence of English and Spanish content,[^6] which makes the LLMs highly homogeneous with the dominant views of the United States, Canada, and Chile societies, while low-aligned countries tend to be in non-Latin-speaking environments, which results in a severe scarcity of their political and cultural alignments in the training set. In addition, the alignment with Slovakia and Mongolia is surprisingly high, which implies that the LLM may have learned indirect social signals to Slovakia and Mongolia by borrowing from regional media or cross-language interconnected corpora, suggesting that estimating cultural alignment purely in terms of corpus size is still insufficient. It is worth noting that although DeepSeek-R1 supports Chinese well, its opinion distribution is not highly aligned with China. In fact, as we show in Appendix D, DeepSeek-R1 exhibits a similar pattern to other LLMs (such as GPT-4) in terms of country’s opinion alignment.

接下来，我们把注意力转到 模型与哪些国家更为对齐 这个问题上。图 2(b) 给出了与 DeepSeek-R1 意见分布对齐程度最高和最低的五个国家的意见分布。[^5] 可以看到，DeepSeek-R1 与美国和智利等国的平均对齐分数最高（分别为 0.8972 和 0.8948），与埃及和缅甸等国的分数相对较低（分别为 0.7855 和 0.7881）。值得注意的是，高对齐国家上对齐分数的标准差显著低于低对齐国家，说明 DeepSeek-R1 在高对齐国家上既 接近 又 稳健 。相比之下，低对齐国家既存在系统性的错位，标准差也更高。这可能是由于英语和西班牙语内容广泛存在，[^6] 使模型与美国、加拿大和智利社会的主流看法高度同质，而低对齐国家往往处于非拉丁语环境，其政治与文化取向在训练集中严重匮乏。此外，与斯洛伐克和蒙古的对齐出人意料地高，这暗示模型可能通过借用区域媒体或跨语言相互关联的语料，间接学到了关于这两个国家的社会信号，说明仅仅按语料规模来估计文化对齐仍然不够。还需要指出的是，尽管 DeepSeek-R1 对中文支持良好，它的意见分布与中国的对齐并不高。事实上，如我们在附录 D 中所示，DeepSeek-R1 在国家意见对齐上表现出与其他模型（如 GPT-4）相似的模式。

4.3 Alignment Difference Level

4.3 对齐差异层面

y=x

y=x

5 Steerability of Language (RQ2)

5 语言的可引导性（RQ2）

t

Method	Aya23	Llama3	Qwen2.5	GPT-3.5	GPT-4	GPT-5	DS-V3	DS-R1	AVG.
China
No Steering (En.)	0.8038	0.8235	0.8273	0.7816	0.8152	0.8348	0.7952	0.8174	0.8124
+Language Steering (Zh.)	0.8103	0.8308	0.8439	0.7976	0.8526∗**	0.8601	0.8471∗∗**	0.8498∗**	0.8365∗∗∗**
Persona Steering (En.)	0.8154	0.8439	0.8316	0.7846	0.8247	0.8953∗∗∗	0.8278∗	0.8625∗∗	0.8357∗∗∗
+Language Steering (Zh.)	0.8216	0.8553∗**	0.8629∗**	0.8088	0.8718∗∗∗**	0.8979∗∗∗**	0.8732∗∗∗**	0.8828∗∗∗**	0.8593∗∗∗**
Few-shot Steering (En.)	0.8115	0.8666∗∗**	0.8596	0.8149	0.8629∗∗	0.8996∗∗∗	0.8775∗∗∗	0.8884∗∗∗	0.8601∗∗∗
+Language Steering (Zh.)	0.8469∗∗**	0.8652∗∗	0.8838∗∗∗**	0.8345∗∗**	0.8961∗∗∗**	0.9041∗∗∗**	0.8884∗∗∗**	0.8976∗∗∗**	0.8771∗∗∗**
Germany
No Steering (En.)	0.8175	0.8441	0.8541	0.7993	0.8468	0.8826	0.8280	0.8543	0.8408
+Language Steering (De.)	0.8445∗**	0.8494	0.8815∗∗**	0.8140	0.8765∗∗**	0.8801	0.8689∗∗**	0.8715	0.8608∗∗**
Persona Steering (En.)	0.8081	0.8510	0.8596	0.8193	0.8736∗	0.9129∗∗∗	0.8731∗∗∗	0.8879∗∗∗	0.8607∗∗∗
+Language Steering (De.)	0.8482∗**	0.8507	0.8904∗∗∗**	0.8250	0.8938∗∗∗**	0.9130∗∗∗**	0.8905∗∗∗**	0.9053∗∗∗**	0.8771∗∗∗**
Few-shot Steering (En.)	0.8345	0.8348	0.8619	0.8352∗	0.8670	0.8983	0.8709∗∗∗	0.8581	0.8576∗∗∗
+Language Steering (De.)	0.8661∗∗∗**	0.8683	0.8916∗∗∗**	0.8476∗∗**	0.9097∗∗∗**	0.9219∗∗∗**	0.9072∗∗∗**	0.9103∗∗∗**	0.8903∗∗∗**

t

方法	Aya23	Llama3	Qwen2.5	GPT-3.5	GPT-4	GPT-5	DS-V3	DS-R1	平均
中国
无引导（英）	0.8038	0.8235	0.8273	0.7816	0.8152	0.8348	0.7952	0.8174	0.8124
+语言引导（中）	0.8103	0.8308	0.8439	0.7976	0.8526∗**	0.8601	0.8471∗∗**	0.8498∗**	0.8365∗∗∗**
人格引导（英）	0.8154	0.8439	0.8316	0.7846	0.8247	0.8953∗∗∗	0.8278∗	0.8625∗∗	0.8357∗∗∗
+语言引导（中）	0.8216	0.8553∗**	0.8629∗**	0.8088	0.8718∗∗∗**	0.8979∗∗∗**	0.8732∗∗∗**	0.8828∗∗∗**	0.8593∗∗∗**
少样本引导（英）	0.8115	0.8666∗∗**	0.8596	0.8149	0.8629∗∗	0.8996∗∗∗	0.8775∗∗∗	0.8884∗∗∗	0.8601∗∗∗
+语言引导（中）	0.8469∗∗**	0.8652∗∗	0.8838∗∗∗**	0.8345∗∗**	0.8961∗∗∗**	0.9041∗∗∗**	0.8884∗∗∗**	0.8976∗∗∗**	0.8771∗∗∗**
德国
无引导（英）	0.8175	0.8441	0.8541	0.7993	0.8468	0.8826	0.8280	0.8543	0.8408
+语言引导（德）	0.8445∗**	0.8494	0.8815∗∗**	0.8140	0.8765∗∗**	0.8801	0.8689∗∗**	0.8715	0.8608∗∗**
人格引导（英）	0.8081	0.8510	0.8596	0.8193	0.8736∗	0.9129∗∗∗	0.8731∗∗∗	0.8879∗∗∗	0.8607∗∗∗
+语言引导（德）	0.8482∗**	0.8507	0.8904∗∗∗**	0.8250	0.8938∗∗∗**	0.9130∗∗∗**	0.8905∗∗∗**	0.9053∗∗∗**	0.8771∗∗∗**
少样本引导（英）	0.8345	0.8348	0.8619	0.8352∗	0.8670	0.8983	0.8709∗∗∗	0.8581	0.8576∗∗∗
+语言引导（德）	0.8661∗∗∗**	0.8683	0.8916∗∗∗**	0.8476∗∗**	0.9097∗∗∗**	0.9219∗∗∗**	0.9072∗∗∗**	0.9103∗∗∗**	0.8903∗∗∗**

Baselines Steerability refers to the ability of LLMs to adjust and align with the opinion of a target demographic group (Meister et al. 2025). Existing steering methods involve prepending additional context to the prompt describing the group we want the model to emulate. We consider the following steering methods as our baselines.

基线 可引导性是指模型调整自身、并与目标人口群体的意见对齐的能力 (Meister et al. 2025)。现有引导方法通常是在提示前附加一段描述，说明我们希望模型去模仿的群体。我们把下列引导方法作为基线。

• Persona Steering operates by inserting a concise persona description into the original task prompt, prompting the LLM to first “locate” itself as a member of a specific group and thereby explicitly emulate that group’s opinion orientation. In this paper, we follow Santurkar et al. (2023) and (Meister et al. 2025), in which the LLM is instructed to pretend to be a member of the target country.

• 人格引导 的做法是，在原来的任务提示中插入一段简短的人格描述，促使模型先把自己“定位”为某一特定群体的成员，从而明确地模仿该群体的意见取向。本文沿用 Santurkar et al. (2023) 和 (Meister et al. 2025) 的设定，让模型被指示假装成为目标国家的一名成员。

• Few Shot Steering (Meister et al. 2025) provides the persona steering setting while also giving five in-context examples of the real group opinion distributions and asking the LLM to emulate the group’s responses. This setting is representative when we already have the opinion distributions of the target group.

• 少样本引导 (Meister et al. 2025) 在人格引导设定的基础上，再给出五个关于真实群体意见分布的上下文示例，并要求模型模仿该群体的回答。当我们已经拥有目标群体的意见分布时，这一设定具有代表性。

l

l

Because WVS provides a multilingual translated version of the survey questionnaire for countries that use multiple languages (e.g., Singapore uses Chinese, English, and Malay; respondents may see the questionnaire in both English and Malay), the survey results from these countries may not reflect the opinions of single-language speakers. Therefore, in order to quantitatively analyze the effect of language steering, we must avoid respondents using multilingual questionnaires. The languages and countries we choose to investigate must satisfy the following conditions: 1) the country must be surveyed by WVS using a single language; and 2) the language must be supported by the LLM. After our selection, our experiments cover Spanish, Chinese, Japanese, Korean, German, Russian, Vietnamese, and Portuguese. As WVS covers survey data from 12 Spanish-speaking countries, we select three countries, Argentina, Chile, and Uruguay, to validate language steering. For other languages, we select one country for each language.

由于 WVS 会为使用多种语言的国家提供多语翻译版问卷（例如新加坡使用中文、英语和马来语；受访者可能同时看到英语和马来语问卷），这些国家的调查结果可能无法反映单一语言使用者的意见。因此，为了定量分析方法的效果，我们必须避开使用多语问卷的受访者。我们所选择考察的语言和国家必须满足以下条件： 1) 该国必须由 WVS 用单一语言进行调查； 2) 该语言必须为模型所支持。经过筛选，我们的实验覆盖西班牙语、中文、日语、韩语、德语、俄语、越南语和葡萄牙语。由于 WVS 覆盖 12 个西班牙语国家的调查数据，我们选择阿根廷、智利和乌拉圭三个国家来验证语言引导。对其余语言，每种语言选择一个国家。

Effectiveness of Steering Table 1 shows the alignment scores for China and Germany under different steering methods for LLMs.[^7] We show these two countries (or languages) due to their representation of non-Latin (Chinese) and Latin writing systems (German), respectively, forming a striking contrast in terms of language families and verifying the universality of the steering methods. We can see that the baseline methods persona steering and few-shot steering show a certain steering ability on all countries compared to no steering. In general, few-shot steering exhibits higher steering ability than persona steering, and our findings are consistent with existing studies (Meister et al. 2025; Studdiford et al. 2025). Moreover, “few-shot + language steering” achieves the highest alignment scores on both countries, which indicates the effectiveness of language in steering LLMs to emulate the opinions of language speakers. Although persona steering can be inaccurate (Meister et al. 2025), “persona + language steering” also yields promising steering ability on China and Germany. In addition, among the LLMs tested, DeepSeek-R1 exhibits the strongest response to the composite strategy, reflecting its greater ability to understand the countries we tested. Overall, language steering is effective in steering LLMs to emulate country-specific opinions. This result implicates that while chasing higher benchmark scores may suggest using a language other than the target one (Kaneko et al. 2025), for opinion-oriented tasks, sticking to the target language can yield better alignment. Therefore, whenever we consider deploying other languages, we must pay careful attention to value alignment and similar factors.

引导的有效性 表 1 给出了不同引导方法下，各模型在中国和德国上的对齐分数。[^7] 我们展示这两个国家（或语言），是因为它们分别代表非拉丁文字（中文）和拉丁文字（德语），在语系上形成鲜明对照，从而验证引导方法的普适性。可以看到，与无引导相比，基线方法人格引导和少样本引导在所有国家上都表现出一定的引导能力。总体而言，少样本引导的引导能力高于人格引导，我们的发现与已有研究一致 (Meister et al. 2025; Studdiford et al. 2025)。此外，“少样本 + 语言引导”在两个国家上都取得了最高对齐分数，说明语言在引导模型模仿该语言使用者意见方面是有效的。尽管人格引导可能不够准确 (Meister et al. 2025)，“人格 + 语言引导”在中国和德国上也表现出有前景的引导能力。另外，在所测试的模型中，DeepSeek-R1 对组合策略的响应最强，反映出它对我们所测试国家有更强的理解能力。总体来看，语言引导能够有效地引导模型去模仿特定国家的意见。这一结果意味着：虽然为了追求更高的基准分数，人们有时会建议使用目标语言以外的语言 (Kaneko et al. 2025)，但在以意见为导向的任务中，坚持使用目标语言往往能得到更好的对齐。因此，每当我们考虑部署其他语言时，都必须仔细关注价值对齐及类似因素。

6 Temporal Opinion Alignment (RQ3)

6 时间维度上的意见对齐（RQ3）

Human cognition has a social historical character (Berger and Luckmann 2016), which means that human cognition is not static; it is constantly being reshaped as historical conditions evolve (Vygotsky 2012; Mannheim 2013). Therefore, it is important to know whether LLMs reflect contemporary human opinions. In this study, we suppose that it is desirable for LLMs to reflect contemporary human opinions . Next, we will show the representativeness of LLMs with human opinion distributions in the temporal dimension.

人类认知具有社会历史性 (Berger and Luckmann 2016)，这意味着人类认知并不是静止的；它会随着历史条件的演进而不断被重塑 (Vygotsky 2012; Mannheim 2013)。因此，了解模型是否反映当代人类意见，是重要的。在本研究中，我们假定 模型应当反映当代人类意见 。接下来，我们将展示模型在时间维度上与人类意见分布的代表性。

Method As shown in the results of Figure 2(c), the LLM is under-aligned with most countries’ opinions. We therefore consider which countries are well aligned by the LLM for comparison. Formally, we use the following equation to filter the countries for comparison:

方法 如图 2(c) 的结果所示，模型对大多数国家的意见对准不足。因此，我们考虑哪些国家被模型对齐得较好，以便进行比较。形式上，我们用下面的公式来筛选用于比较的国家：

\mathcal{C}^{*}=\{\,c\in\mathcal{C}\mid\left|\mathcal{A}_{\mathcal{M}}^{(c)}-\mathcal{A}_{avg}^{(c)}\right|<\tau\,\}. \tag{3}

\mathcal{A}_{\mathcal{M}}^{(c)}

\mathcal{A}_{\mathcal{M}}^{(c)}

Results Figure 3: The trend of the average alignment scores of GPT-5 and DeepSeek-R1 with countries filtered using Eq. (3) across waves. The shaded area indicates the standard deviation of the alignment scores at the current wave.

结果 图 3：用式 (3) 筛选出的国家上，GPT-5 与 DeepSeek-R1 跨各波次的平均对齐分数趋势。阴影区域表示当前波次对齐分数的标准差。

Figure 3 illustrates the trend of the average alignment scores of GPT-5 and DeepSeek-R1 across waves. We can see that LLMs most align with the latest wave of human opinions. One reason may be due to the fact that most of the human feedback in the fine-tuning of LLMs comes from contemporary annotators, whose ethical and social orientations are more in line with recent public opinions. The models are rewarded for emulating these behaviors. Another reason may be due to the development of mobile internet and social media, which provide more accessible data than in the past. In addition, the standard deviation reflects an LLM’s coverage degree of the represented opinions. A lower standard deviation indicates a better coverage degree of the opinions. Therefore, LLMs achieve better coverage of human opinions in the latest wave and poorer coverage of human opinions in earlier waves. This further indicates that the alignment of LLMs is influenced by contemporary annotators’ human feedback. In contrast, it implies that there may be challenges for LLMs to align to earlier human opinions. The reason is that it is more difficult to access earlier customized human feedback data.

图 3 展示了 GPT-5 与 DeepSeek-R1 跨各波次的平均对齐分数趋势。可以看到，模型与最近一轮人类意见最为对齐。一个原因可能是，模型微调中的大多数人类反馈来自当代标注者，他们的伦理与社会取向更贴近近期的公众意见。模型因模仿这些行为而得到奖励。另一个原因可能是移动互联网和社交媒体的发展，提供了比过去更容易获得的数据。此外，标准差反映了模型对所表征意见的覆盖程度。标准差更低，表示对意见的覆盖更好。因此，模型在最近一轮上对人类意见的覆盖更好，在较早轮次上的覆盖则较差。这进一步表明，模型的对齐受到当代标注者人类反馈的影响。相对地，这也意味着模型要对齐更早时期的人类意见，可能面临挑战。原因是，更早的、定制化的人类反馈数据更难获得。

7 Discussion

7 讨论

7.1 Alignment of Human Opinions across Countries

7.1 各国人类意见之间的对齐

To understand the alignment between different countries’ opinions, we show the alignment scores between countries and countries in Figure 4. Similar to Figure 2(b), we plot the first and last 6 countries ranked by their alignment scores with DeepSeek-R1’s opinion distributions. For comparison, we also show the alignment scores between the countries and DeepSeek-R1 in the results. More results can be found in Appendix G.1. The alignment scores of opinion distributions among the first 6 ranking countries are generally high, indicating significant similarity in their views. In contrast, the alignment scores among the last 6 ranking countries show relative differences. For example, Jordan, Libya, and Egypt have high alignment scores with each other, while Bangladesh, China, and Myanmar have low alignment scores with other countries. Meanwhile, the alignment scores between the first and last 6 ranking countries are generally low. Furthermore, by calculating the alignment scores between DeepSeek-R1 and each country, we find that despite some heterogeneity in alignment scores among the first 6 ranking countries, DeepSeek-R1’s alignment scores with these countries are surprisingly consistent. This pattern suggests that the model captures an “average” cross-country view rather than aligning specifically to individual countries, revealing a lack of country-specific sensitivity in the alignment of opinion distributions.

为了理解不同国家意见之间的对齐，我们在图 4 中展示了国家与国家之间的对齐分数。与图 2(b) 类似，我们画出按与 DeepSeek-R1 意见分布的对齐分数排序、位列最前和最后的 6 个国家。为便于比较，结果中也给出了这些国家与 DeepSeek-R1 之间的对齐分数。更多结果见附录 G.1。排名最前的 6 个国家之间，意见分布的对齐分数普遍较高，说明它们的看法有显著相似性。相比之下，排名最后的 6 个国家之间的对齐分数则表现出相对差异。例如，约旦、利比亚和埃及彼此之间对齐分数较高，而孟加拉国、中国和缅甸与其他国家的对齐分数较低。与此同时，排名最前与最后的 6 个国家之间的对齐分数普遍较低。此外，通过计算 DeepSeek-R1 与每个国家的对齐分数，我们发现：尽管排名最前的 6 个国家彼此之间的对齐分数存在一定异质性，DeepSeek-R1 与这些国家的对齐分数却出人意料地一致。这一模式表明，模型捕捉的是一种“平均”的跨国看法，而不是专门与个别国家对齐，从而揭示出意见分布对齐中缺少针对具体国家的敏感性。

Figure 4: Alignment scores among different countries. For comparison, we also show the alignment scores between the countries and DeepSeek-R1 (DS-R1).

图 4：不同国家之间的对齐分数。为便于比较，我们也给出了这些国家与 DeepSeek-R1（DS-R1）之间的对齐分数。

7.2 Internal Consistency of LLMs

7.2 大语言模型的内部一致性

In order to investigate whether LLMs hold self-contradictory opinions when answering questions, we conduct a closer look at the internal consistency of LLMs. Concretely, we manually select four questions about gender fairness (agree or disagree that men are superior to women), four questions about atheism (believe or disbelieve in theism), and two questions about democracy (important or not important that democracy).[^8] Questions with the same topic reflect the same logic of opinion judgment. In our experiments, we consider the options “Strong agree” and “Agree,” which both express positive attitudes, to be consistent opinions. For options related to degree (e.g., from “1. Not at all important” to “10. Absolutely important”), we group options 1 to 5 and options 6 to 10 as consistent opinion groups, respectively.

为了考察模型在回答问题时是否持有自相矛盾的意见，我们对模型的内部一致性做了更仔细的观察。具体而言，我们人工选出四道关于性别公平的题目（同意或不同意男性优于女性），四道关于无神论的题目（相信或不相信有神论），以及两道关于民主的题目（民主重要或不重要）。[^8] 同一主题下的题目，反映的是同一套意见判断逻辑。在实验中，我们把同属正面态度的选项“非常同意”和“同意”视为一致的意见。对于与程度相关的选项（例如从“1. 完全不重要”到“10. 绝对重要”），我们分别把选项 1 至 5 和选项 6 至 10 归为一致的意见组。

We use the internal consistency rate, which is the ratio of answers within the same opinion group, as the evaluation measure. If the answers to a set of questions are [1, 1, 2, 1], the internal consistency rate is 75%. The experimental results show that on the topic of gender fairness, all the LLMs remain at a 100% internal consistency rate. However, GPT-3.5-Turbo shows only 50% internal consistency on the topics of atheism and democracy, in contrast to other LLMs, which maintain full consistency. This suggests that, relative to other LLMs, GPT-3.5-Turbo not only poorly aligns with human opinions (§4), but also lacks internal consistency.

我们用内部一致率作为评价指标，即落在同一意见组内的答案所占比例。如果一组问题的答案是 [1, 1, 2, 1]，则内部一致率为 75%。实验结果表明，在性别公平这一主题上，所有模型都保持 100% 的内部一致率。然而，GPT-3.5-Turbo 在无神论和民主这两个主题上的内部一致率只有 50%，而其他模型仍保持完全一致。这说明，相对于其他模型，GPT-3.5-Turbo 不仅与人类意见对齐较差（§4），也缺乏内部一致性。

7.3 Sensitivity of LLMs

7.3 大语言模型的敏感性

r

Models	Order	#few-shot	Prob.
Aya23	0.9660	0.9893	0.9815
Llama3	0.9927	0.9964	0.9933
Qwen2.5	0.9928	0.9928	0.9973
GPT-3.5	0.9852	0.9810	0.9736
GPT-4	0.9925	0.9783	0.9902
GPT-5	0.9973	0.9968	0.9980
DeepSeek-V3	0.9895	0.9805	0.9881
DeepSeek-R1	0.9885	0.9861	0.9924

r

模型	顺序	少样本数量	概率
Aya23	0.9660	0.9893	0.9815
Llama3	0.9927	0.9964	0.9933
Qwen2.5	0.9928	0.9928	0.9973
GPT-3.5	0.9852	0.9810	0.9736
GPT-4	0.9925	0.9783	0.9902
GPT-5	0.9973	0.9968	0.9980
DeepSeek-V3	0.9895	0.9805	0.9881
DeepSeek-R1	0.9885	0.9861	0.9924

We acknowledge that current LLMs are very sensitive to the prompt format (Zhuo et al. 2024). Inspired by Santurkar et al. (2023), we test the prompt sensitivity of the LLMs used in our experiments against the following factors:

我们承认，当前的大语言模型对提示格式非常敏感 (Zhuo et al. 2024)。受 Santurkar et al. (2023) 的启发，我们针对下列因素测试了本实验所用模型的提示敏感性：

• The order in which the question options are presented to the model;

• 向模型呈现问题选项的次序；

• The few-shot examples (the number of the few-shot examples and the probability distribution of the few-shot examples).

• 少样本示例（少样本示例的数量，以及少样本示例的概率分布）。

r

r

8 Related Work

8 相关工作

Human Opinion and Culture. Culture can be defined as the pattern of thinking, feeling, and reacting, distinguishing human groups (Wallace and Fogelson 1961; Shweder et al. 2007; Cao et al. 2023). Human opinions are the beliefs, preferences, and judgments held by individuals, and are deeply influenced by cultural backgrounds (Peterson 2003; Markus and Kitayama 2014). Culture provides people with a shared framework through which they interpret experiences, internalize norms, and express their opinions (Geertz 2017). It is challenging to align the opinions of human societies with different cultural backgrounds (Hall 1976).

人类意见与文化。 文化可以被定义为区分人类群体的思维、感受和反应模式 (Wallace and Fogelson 1961; Shweder et al. 2007; Cao et al. 2023)。人类意见是个人所持有的信念、偏好和判断，并深受文化背景影响 (Peterson 2003; Markus and Kitayama 2014)。文化为人们提供了一个共享框架，他们通过这一框架解释经验、内化规范并表达意见 (Geertz 2017)。要对齐具有不同文化背景的人类社会的意见，是一项挑战 (Hall 1976)。

Culture in LLMs. Previous studies have shown that LLMs exhibit cultural challenges similar to those found in human society (Li et al. 2024). Cecilia Liu et al. (2024) pointed out that there is a cultural gap when using LLMs, especially when dealing with content translated from other languages. After that, Masoud et al. (2025) demonstrated that GPT-4 exhibits remarkable capabilities in adapting to these cultural gaps, particularly in the Chinese context. Furthermore, Wang et al. (2024) showed that LLMs have cultural dominance issues because they are mainly trained using English data. However, recent study (Meister et al. 2025) has challenged the existing use of LLMs to emulate opinion distributions, proving that LLMs are more accurate when describing opinion distributions. Therefore, one technical difference from existing studies is that we deployed this method of describing opinion distribution (known as verbalized distribution).

大语言模型中的文化。 先前研究已经表明，大语言模型表现出与人类社会中类似的文化挑战 (Li et al. 2024)。Cecilia Liu et al. (2024) 指出，在使用模型时存在文化差距，尤其是在处理从其他语言翻译而来的内容时。此后，Masoud et al. (2025) 证明，GPT-4 在适应这些文化差距方面表现出显著能力，尤其是在中文语境中。此外，Wang et al. (2024) 表明，由于模型主要用英语数据训练，它们存在文化主导问题。然而，最近的研究 (Meister et al. 2025) 对现有用模型去模仿意见分布的做法提出了质疑，并证明模型在描述意见分布时更为准确。因此，与已有研究的一个技术差异是，我们采用了这种描述意见分布的方法（即 口头化分布 ）。

Opinion of LLMs. LLMs often respond to subjective questions in a way that implicitly favors certain human groups. Early study (Santurkar et al. 2023) quantified the differences between LLMs’ predicted distributions of response options and human distributions based on surveys. Meister et al. (2025) expanded the assessment scope to multiple demographic dimensions and compared different distribution expressions (model log-probability, sequence of tokens, and verbalized distribution). Building on this research direction, this study expands the scope from a few countries to a truly worldwide country sample and explicitly introduces the temporal dimension.

大语言模型的意见。 大语言模型在回答主观问题时，常常以隐含偏向某些人类群体的方式作答。早期研究 (Santurkar et al. 2023) 基于调查，量化了模型所预测的作答选项分布与人类分布之间的差异。Meister et al. (2025) 把评估范围扩展到多个人口维度，并比较了不同的分布表达方式（模型对数概率、词元序列和口头化分布）。沿着这一研究方向，本研究把范围从少数几个国家扩展到真正覆盖全球的国家样本，并明确引入了时间维度。

Steering Methods. To enable LLMs to emulate specific groups, previous studies (Meister et al. 2025) have explored persona and few-shot steering. However, persona steering is not always accurate, leading to undesirable effects such as stereotypes and exacerbated polarization (Perez et al. 2023; Cheng et al. 2023; Wang et al. 2025). We further expanded on these two methods and proposed language steering: changing the language of the prompt can significantly improve alignment with the opinion distribution of the corresponding country, indicating that language itself, as an alignment cue, can drive opinion alignment.

引导方法。 为了使模型能够模仿特定群体，先前研究 (Meister et al. 2025) 探索了人格引导和少样本引导。然而，人格引导并不总是准确，会带来刻板印象和极化加剧等不良效果 (Perez et al. 2023; Cheng et al. 2023; Wang et al. 2025)。我们在这两种方法上做了进一步扩展，并提出语言引导：改变提示语言可以显著改善与相应国家意见分布的对齐，说明语言本身作为一种对齐线索，能够推动意见对齐。

9 Conclusion

9 结论

In this paper, we propose a framework to investigate the global human opinion alignment of LLMs using the WVS questionnaires and data. We manually filter the 259 questions in the WVS questionnaires across multiple language versions and historical periods to build our dataset. Through experimenting with LLMs on our data, our main findings are as follows: 1) LLMs only appropriately align with a few countries, while under-aligning with most countries. 2) Language can significantly steer LLMs to emulate country-specific opinions. 3) LLMs most align with the contemporary human opinions. Our study also discusses various aspects of LLMs, such as internal consistency and sensitivity of LLMs. This paper will provide insights for studies related to the value alignment of LLMs.

本文提出一套框架，利用 WVS 问卷和数据来考察大语言模型与全球人类意见的对齐。我们人工筛选了 WVS 问卷中跨多种语言版本和历史时期的 259 道题目，以构建数据集。通过在我们的数据上对模型做实验，主要发现如下： 1) 模型只与少数国家对齐得当，而对大多数国家对准不足。 2) 语言能够显著引导模型去模仿特定国家的意见。 3) 模型与当代人类意见最为对齐。本研究还讨论了模型的多个方面，例如内部一致性和敏感性。本文将为与大语言模型价值对齐相关的研究提供启示。

Acknowledgments

致谢

We thank the reviewers for their valuable feedback. This work was supported by JST BOOST, Grant Number JPMJBS2407.

我们感谢审稿人提供的宝贵意见。本工作得到 JST BOOST 的支持，资助号为 JPMJBS2407。

References

参考文献

Wei et al. (2021) J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le Finetuned language models are zero-shot learners . arXiv preprint arXiv:2109.01652 . Cited by: §1 . Zhuo et al. (2024) J. Zhuo, S. Zhang, X. Fang, H. Duan, D. Lin, and K. Chen ProSA: assessing and understanding the prompt sensitivity of llms . arXiv preprint arXiv:2410.12405 . Cited by: §7.3 .

Appendix A More Details of The World Values Survey

附录 A 世界价值观调查的更多细节

Collecting data on human opinions across global, language, and temporal dimensions is costly. The WVS is an international academic project studying human values, beliefs, norms, self-descriptions, and other elements of national culture, as well as political attitudes and opinions, across the globe. The scholars who lead the project are mainly interested in the relationships between economic development, cultural change, and political life on all continents. The WVS data have also been widely used by cross-cultural psychologists, anthropologists, and other social scientists (Minkov 2012). The project started in 1981 and has been operating in more than 120 world countries and regions. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years (Haerpfer et al. 2022). The latest wave of the survey employs a standardized questionnaire containing approximately 259 subjective questions. Its broad regional and topical coverage, as well as its free public access to survey data and research results, makes the WVS one of the most authoritative and widely used multi-country surveys in the field of social sciences.

在全球、语言和时间三个维度上收集人类意见数据，成本很高。WVS 是一项国际学术计划，研究全球范围内的人类价值观、信念、规范、自我描述以及国家文化的其他要素，也包括政治态度和意见。主持该计划的学者，主要关注各大洲经济发展、文化变迁与政治生活之间的关系。WVS 数据也被跨文化心理学家、人类学家和其他社会科学家广泛使用 (Minkov 2012)。该计划始于 1981 年，已在全球 120 多个国家和地区开展。项目的主要研究工具是一项具有代表性的比较社会调查，每 5 年在全球开展一次 (Haerpfer et al. 2022)。最新一轮调查采用标准化问卷，约含 259 道主观题。其广泛的地区与主题覆盖，以及对调查数据和研究结果的免费公开获取，使 WVS 成为社会科学领域最具权威、使用最广的多国调查之一。

Appendix B Experiment Settings

附录 B 实验设置

B.1 Hyperparameter

B.1 超参数

The hyperparameter settings for the LLMs (if available) are shown in Table 3.

各模型的超参数设置（若可获得）如表 3 所示。

p

Hyperparameter	Assignment
Top- $p$	1.0
Temperature	0.0
Max new tokens	256
Frequency penalty	0.0
Presence penalty	0.0

p

超参数	取值
Top- $p$	1.0
Temperature	0.0
Max new tokens	256
Frequency penalty	0.0
Presence penalty	0.0

For GPT-5, we set the reasoning configuration to reasoning = {effort = ‘‘low’’} and the text generation configuration to text = {verbosity = ‘‘low’’} to obtain concise responses with reduced reasoning depth.

对于 GPT-5，我们将推理配置设为 reasoning = {effort = ‘‘low’’}，将文本生成配置设为 text = {verbosity = ‘‘low’’}，以获得推理深度较低、更为简洁的回答。

B.2 Question Filter Rules

B.2 题目筛选规则

The wave 7 questionnaire contains 259 questions, but not all of them are suitable for LLMs to answer. If the questions are not selected appropriately, it may cause bias in the evaluation of LLMs. Therefore, we first filter the questions. We filter out the following types of questions from the WVS questionnaire:

第 7 波问卷包含 259 道题目，但并非全部都适合让大语言模型作答。如果题目选择不当，可能会给模型评测带来偏差。因此，我们首先筛选题目。我们从 WVS 问卷中滤除以下几类题目：

Not multiple choice. For example, questions Q7 to Q17 each contain two options (“Mentioned” and “Not mentioned”), and the questionnaire requires respondents to choose up to 5 mentioned options from Q7 to Q17.

非选择题。 例如，题目 Q7 至 Q17 各含两个选项（“提及”和“未提及”），问卷要求受访者从 Q7 至 Q17 中至多选择 5 个被提及的选项。

Require life experience to answer. Some questions require life experience to answer. Therefore, they are not suitable for LLMs to answer. For example, questions Q51 to Q55 begin with “In the last 12 months.”

需要生活经验才能作答。 有些题目需要生活经验才能作答，因此不适合让模型回答。例如，题目 Q51 至 Q55 以“在过去 12 个月里”开头。

Need to edit slot. Some questions need to edit a slot according to the respondent’s country. For example, Q64 needs to modify the slot [churches] according to the respondent’s country. In the “No steering” scenario, we do not want to leak any country information to LLMs.

需要改写槽位。 有些题目需要根据受访者所在国家改写一个槽位。例如，Q64 需要根据受访者所在国家修改槽位 [churches]。在“无引导”设定中，我们不希望向模型泄露任何国家信息。

Objective question. Some questions are objective questions. For example, question Q91, “ Five countries have permanent seats on the Security Council of the United Nations. Which one of the following is not a member? A) France, B) China, C) India .”

客观题。 有些题目是客观题。例如，题目 Q91：“ 联合国安理会有五个常任理事国。下列哪一个国家不是成员国？A) 法国，B) 中国，C) 印度 。”

Require a nationality. Some questions require the respondents to have a nationality, so they are not suitable for LLMs to answer. For example, question Q120, “ How high is the risk in this country to be held accountable for giving or receiving a bribe, gift or favor in return for public service? To indicate your opinion, use a 10-point scale where ‘1’ means ‘no risk at all’ and ‘10’ means ‘very high risk’ .”

需要具有某一国籍。 有些题目要求受访者具有某一国籍，因此不适合让模型回答。例如，题目 Q120：“ 在这个国家，因提供或收受贿赂、礼物或好处以换取公共服务而被追究责任的风险有多高？请用 10 点量表表明你的看法，其中‘1’表示‘完全没有风险’，‘10’表示‘风险非常高’ 。”

We put all the filtered 144 questions in the WV7_English.jsonl file under the dataset/questions folder. The naming convention for saving files with questions in other languages is to replace English with the other language (e.g., German). Table 4 is the explanation of each column in the jsonl file.

我们把筛选后的全部 144 道题目放在 dataset/questions 文件夹下的 WV7_English.jsonl 文件中。保存其他语言题目的文件命名约定是，把 English 替换成相应语言（例如 German）。表 4 是对该 jsonl 文件中各列的说明。

Names Descriptions id The question id question The question description choice_keys The option number choices The option content answer The combined option number and option content Table 4: Names and descriptions of columns in jsonl file.

Names	Descriptions
id	The question id
question	The question description
choice_keys	The option number
choices	The option content
answer	The combined option number and option content

名称 说明 id 题目编号 question 题目描述 choice_keys 选项编号 choices 选项内容 answer 选项编号与选项内容的组合 表 4：jsonl 文件中各列的名称与说明。

名称	说明
id	题目编号
question	题目描述
choice_keys	选项编号
choices	选项内容
answer	选项编号与选项内容的组合

Country Example IDs Default (English version) Q60, Q70, Q90, Q110, Q130 Argentina Q40, Q60, Q80, Q150, Q160 Brazil Q40, Q60, Q80, Q150, Q160 Chile Q40, Q60, Q80, Q150, Q160 China Q60, Q70, Q110, Q150, Q160 Germany Q40, Q80, Q150, Q160, Q170 Japan Q60, Q70, Q90, Q110, Q130 Korea Q40, Q80, Q150, Q160, Q170 Russia Q40, Q60, Q80, Q150, Q160 Uruguay Q40, Q60, Q80, Q150, Q160 Vietnam Q40, Q60, Q80, Q150, Q160 Table 5: The question ids of the few-shot examples we selected for each country.

Country	Example IDs
Default (English version)	Q60, Q70, Q90, Q110, Q130
Argentina	Q40, Q60, Q80, Q150, Q160
Brazil	Q40, Q60, Q80, Q150, Q160
Chile	Q40, Q60, Q80, Q150, Q160
China	Q60, Q70, Q110, Q150, Q160
Germany	Q40, Q80, Q150, Q160, Q170
Japan	Q60, Q70, Q90, Q110, Q130
Korea	Q40, Q80, Q150, Q160, Q170
Russia	Q40, Q60, Q80, Q150, Q160
Uruguay	Q40, Q60, Q80, Q150, Q160
Vietnam	Q40, Q60, Q80, Q150, Q160

国家 示例编号 默认（英文版） Q60, Q70, Q90, Q110, Q130 阿根廷 Q40, Q60, Q80, Q150, Q160 巴西 Q40, Q60, Q80, Q150, Q160 智利 Q40, Q60, Q80, Q150, Q160 中国 Q60, Q70, Q110, Q150, Q160 德国 Q40, Q80, Q150, Q160, Q170 日本 Q60, Q70, Q90, Q110, Q130 韩国 Q40, Q80, Q150, Q160, Q170 俄罗斯 Q40, Q60, Q80, Q150, Q160 乌拉圭 Q40, Q60, Q80, Q150, Q160 越南 Q40, Q60, Q80, Q150, Q160 表 5：我们为每个国家所选少样本示例的题目编号。

国家	示例编号
默认（英文版）	Q60, Q70, Q90, Q110, Q130
阿根廷	Q40, Q60, Q80, Q150, Q160
巴西	Q40, Q60, Q80, Q150, Q160
智利	Q40, Q60, Q80, Q150, Q160
中国	Q60, Q70, Q110, Q150, Q160
德国	Q40, Q80, Q150, Q160, Q170
日本	Q60, Q70, Q90, Q110, Q130
韩国	Q40, Q80, Q150, Q160, Q170
俄罗斯	Q40, Q60, Q80, Q150, Q160
乌拉圭	Q40, Q60, Q80, Q150, Q160
越南	Q40, Q60, Q80, Q150, Q160

B.3 Full Prompts

B.3 完整提示

We follow the settings of Meister et al. (2025) by providing few-shot examples to control the LLMs’ output format. Our prompts follow the following format:

我们沿用 Meister et al. (2025) 的设定，通过提供少样本示例来控制模型的输出格式。我们的提示采用如下格式：

The task instruction part will slightly differ depending on the steering method. For example, in the case of persona steering, the task instruction will require the LLMs to emulate people from a specific country (e.g., Germany). Note that in the case of language steering, the task instruction, few-shot examples, and input question are all replaced with questions in the target language or translated into the target language.

任务指令部分会因引导方法而略有不同。例如，在人格引导的情形下，任务指令会要求模型去模仿来自某一特定国家（例如德国）的人。需要注意的是，在语言引导的情形下，任务指令、少样本示例和输入问题都会被替换成目标语言的题目，或被翻译成目标语言。

When selecting few-shot examples, there may be missing opinion distributions for some questions depending on the country. Table 5 lists the question ids of the few-shot examples we selected for each country.

在选择少样本示例时，某些题目的意见分布可能因国家而缺失。表 5 列出了我们为每个国家所选少样本示例的题目编号。

In addition, all few-shot examples are available under the input\express_distribution folder. Table 6 lists each file and its corresponding explanation.

此外，全部少样本示例都可以在 input\express_distribution 文件夹下获得。表 6 列出了每个文件及其相应说明。

File Name Steering Method lang-En_dist-random.txt no steering, persona steering lang-En_dist-{country}.txt few-shot steering lang-{language}_dist-random.txt language steering persona + language steering lang-{language}_dist-{country}.txt few-shot + language steering Table 6: The few-shot examples under the inputs/few_shot folder. Here, {language} represents the language of the prompt, which can be De (German), En (English), Es (Spanish), Ja (Japanese), Ko (Korean), Pt (Portuguese), Ru (Russian), Vi (Vietnamese), and Zh (Chinese). The random means that the distribution of examples is random, and {country} means that the distribution of examples is the opinion distribution of that country.

File Name	Steering Method
lang-En_dist-random.txt	no steering, persona steering
lang-En_dist-{country}.txt	few-shot steering
lang-{language}_dist-random.txt	language steering persona + language steering
lang-{language}_dist-{country}.txt	few-shot + language steering

文件名 引导方法 lang-En_dist-random.txt 无引导，人格引导 lang-En_dist-{country}.txt 少样本引导 lang-{language}_dist-random.txt 语言引导 人格 + 语言引导 lang-{language}_dist-{country}.txt 少样本 + 语言引导 表 6：inputs/few_shot 文件夹下的少样本示例。这里，{language} 表示提示所用语言，可以是 De（德语）、En（英语）、Es（西班牙语）、Ja（日语）、Ko（韩语）、Pt（葡萄牙语）、Ru（俄语）、Vi（越南语）和 Zh（中文）。random 表示示例的分布是随机的，{country} 表示示例的分布是该国的意见分布。

文件名	引导方法
lang-En_dist-random.txt	无引导，人格引导
lang-En_dist-{country}.txt	少样本引导
lang-{language}_dist-random.txt	语言引导人格 + 语言引导
lang-{language}_dist-{country}.txt	少样本 + 语言引导

B.4 More Details of Distribution Expression Methods

B.4 分布表达方法的更多细节

Model log probability. A common method for estimating the choice distribution is to consider the model’s log probabilities of the next token for each option token (e.g., ‘1’, ‘2’, etc.) as a class distribution and sample from it. This method is a standard practice (Santurkar et al. 2023). However, previous studies have shown that the probabilities derived from LLMs are usually much sharper than real human opinion distribution, i.e., most of the probabilities are concentrated in a few answers (Durmus et al. 2024).

模型对数概率。 估计选择分布的一种常见方法，是把模型对每个选项词元（例如‘1’、‘2’等）的下一词对数概率看作类别分布，并从中采样。这是一种标准做法 (Santurkar et al. 2023)。然而，先前研究已经表明，由模型得到的概率通常比真实的人类意见分布尖锐得多，也就是说，大部分概率集中在少数几个答案上 (Durmus et al. 2024)。

Sequence of tokens. Another method is to directly let the model “act as a sampler.” Specifically, we instruct the LLM to output 30 samples from its internal distribution at once (e.g., 1124243412434213). This method is more convenient when generating samples from the opinion distribution for emulation. However, since we use a limited-length sequence to approximate the underlying distribution, the estimation accuracy may be subject to errors due to limits on sample size (Meister et al. 2025).

词元序列。 另一种方法是直接让模型“充当采样器”。具体而言，我们指示模型一次性从其内部分布中输出 30 个样本（例如 1124243412434213）。在需要从意见分布中生成样本以进行模仿时，这种方法更为方便。然而，由于我们用有限长度的序列来近似潜在分布，估计精度可能会因样本量限制而产生误差 (Meister et al. 2025)。

Verbalized distribution. This method directly allows the LLM to verbalize the distribution in JSON format in their output (e.g., {1: 31%, 2: 4%, 3: 30%, 4: 35%}), without any estimation steps or post-processing (Meister et al. 2025). Meister et al. (2025)’s analysis reveals that “verbalized distribution” outperforms the other “model log probability” and “sequence of tokens”. Therefore, we use “verbalized distribution” to represent the opinion distributions in our study.

口头化分布。 这种方法直接让模型在输出中以 JSON 格式口头化该分布（例如 {1: 31%, 2: 4%, 3: 30%, 4: 35%}），不需要任何估计步骤或后处理 (Meister et al. 2025)。Meister et al. (2025) 的分析表明，“口头化分布”优于另外两种“模型对数概率”和“词元序列”。因此，我们在研究中用“口头化分布”来表示意见分布。

Appendix C Complete Country List

附录 C 完整国家列表

Wave 7 of the WVS covers 66 countries or regions. Here is the complete list, with the languages supported in parentheses. The contents in “()” are the language(s) in which the questionnaire is available for each country.

WVS 第 7 波覆盖 66 个国家或地区。下面是完整列表，括号中给出所支持的语言。“()”中的内容是每个国家问卷所提供的一种或多种语言。

Andorra (Catalan, English, French, Spanish), Argentina (Spanish), Armenia (Armenian), Australia (English), Bangladesh (Bangla), Bolivia (Spanish), Brazil (Portuguese), Canada (English, French), Colombia (Spanish), Cyprus (Greek, Turkish), Czechia (Czech), Chile (Spanish), China (Chinese), Ecuador (Spanish), Egypt (Arabic), Ethiopia (Afan Oromo, Amharic, Tigrinya), Germany (German), Greece (Greek), Guatemala (Spanish), Hong Kong SAR (Chinese, English), India (Bengali, English, Hindi, Marathi, Punjabi, Telugu), Indonesia (Indonesian), Iran (Farsi), Iraq (Arabic), Japan (Japanese), Jordan (Arabic), Kazakhstan (Kazakh, Russian), Kenya (Swahili), Kyrgyzstan (Kyrgyz, Russian), Lebanon (Arabic), Libya (Arabic), Macau SAR (Chinese), Malaysia (Chinese, Malay), Maldives (Dhivehi), Mexico (Spanish), Mongolia (Mongolian), Morocco (Arabic), Myanmar (Burmese), Netherlands (Dutch), New Zealand (English), Nicaragua (Spanish), Nigeria (Hausa, Igbo, Yoruba), Pakistan (Urdu), Peru (Spanish), Philippines (Bicol, Cebuano, Filipino, Hiligaynon, Iluko, Tausug, Waray), Puerto Rico (Spanish), Romania (Romanian), Russia (Russian), Serbia (Serbian), Singapore (Chinese, English, Malay), Slovakia (Slovak), South Korea (Korean), Taiwan (Chinese), Tajikistan (Russian, Tajik), Thailand (Thai), Tunisia (Arabic), Turkey (Turkish), Ukraine (Russian, Ukrainian), United Kingdom Great Britain (English), United Kingdom Northern Ireland (English), Uruguay (Spanish), United States (English), Uzbekistan (Uzbek), Venezuela (Spanish), Vietnam (Vietnamese), and Zimbabwe (Ndebele, Shona).

Appendix D More Results of RQ1

附录 D RQ1 的更多结果

D.1 Country Level

D.1 国家层面

We provide the alignment scores for all seven LLMs with different countries; the checklist is shown in Table 7.

我们给出全部七个模型与不同国家的对齐分数；一览表如表 7 所示。

Figure 5: The alignment scores between Aya23 and different countries.

图 5：Aya23 与不同国家之间的对齐分数。

Figure 6: The alignment scores between Qwen2.5 and different countries.

图 6：Qwen2.5 与不同国家之间的对齐分数。

Figure 7: The alignment scores between Llama3 and different countries.

图 7：Llama3 与不同国家之间的对齐分数。

Figure 8: The alignment scores between GPT-3.5 and different countries.

图 8：GPT-3.5 与不同国家之间的对齐分数。

Figure 9: The alignment scores between GPT-4 and different countries.

图 9：GPT-4 与不同国家之间的对齐分数。

Figure 10: The alignment scores between GPT-5 and different countries.

图 10：GPT-5 与不同国家之间的对齐分数。

Figure 11: The alignment scores between DeepSeek-V3 and different countries.

图 11：DeepSeek-V3 与不同国家之间的对齐分数。

Figure 12: The alignment scores between DeepSeek-R1 and different countries.

图 12：DeepSeek-R1 与不同国家之间的对齐分数。

Models Figures Aya23 Figure 5 Qwen2.5 Figure 6 Llama3 Figure 7 GPT-3.5 Figure 8 GPT-4 Figure 9 GPT-5 Figure 10 DeepSeek-V3 Figure 11 DeepSeek-R1 Figure 12 Table 7: Checklist of alignment scores between LLMs and different countries.

Models	Figures
Aya23	Figure 5
Qwen2.5	Figure 6
Llama3	Figure 7
GPT-3.5	Figure 8
GPT-4	Figure 9
GPT-5	Figure 10
DeepSeek-V3	Figure 11
DeepSeek-R1	Figure 12

模型 图 Aya23 图 5 Qwen2.5 图 6 Llama3 图 7 GPT-3.5 图 8 GPT-4 图 9 GPT-5 图 10 DeepSeek-V3 图 11 DeepSeek-R1 图 12 表 7：各模型与不同国家对齐分数的一览表。

模型	图
Aya23	图 5
Qwen2.5	图 6
Llama3	图 7
GPT-3.5	图 8
GPT-4	图 9
GPT-5	图 10
DeepSeek-V3	图 11
DeepSeek-R1	图 12

By comparing Figures 5 to 12, we find that these models have high alignment scores with the United States, Chile, Thailand, Canada, Mongolia, and Slovakia. However, they have low alignment scores with Bangladesh, Myanmar, Libya, and Egypt.

通过比较图 5 至图 12，我们发现这些模型与美国、智利、泰国、加拿大、蒙古和斯洛伐克的对齐分数较高，而与孟加拉国、缅甸、利比亚和埃及的对齐分数较低。

Intuition. If we perform a rough clustering based on our intuition about regions, we can see some trends: LLMs are more aligned with English-speaking countries and several Latin American and Central and Eastern European countries (e.g., the United States, Canada, Chile, the Czech Republic, Slovakia, and Uruguay). They are less aligned with several large Asian countries (e.g., China, India, and Indonesia).

直观看法。 如果根据我们对地区的直观认识做一次粗略聚类，可以看到一些趋势：模型与英语国家和若干拉丁美洲及中东欧国家更为对齐（例如美国、加拿大、智利、捷克、斯洛伐克和乌拉圭）。它们与若干亚洲大国（例如中国、印度和印度尼西亚）的对齐则较弱。

D.2 Alignment Difference Level

D.2 对齐差异层面

We provide the relationship of different countries to all seven LLMs’ opinion distribution and the average human opinion distribution alignment scores; the checklist is shown in Table 8.

我们给出不同国家分别相对于全部七个模型的意见分布、以及相对于人类平均意见分布的对齐分数关系；一览表如表 8 所示。

Figure 13: The relationship of different countries to Aya23’s opinion distribution and the average human opinion distribution alignment scores.

图 13：不同国家分别相对于 Aya23 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 14: The relationship of different countries to Qwen2.5’s opinion distribution and the average human opinion distribution alignment scores.

图 14：不同国家分别相对于 Qwen2.5 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 15: The relationship of different countries to Llama3’s opinion distribution and the average human opinion distribution alignment scores.

图 15：不同国家分别相对于 Llama3 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 16: The relationship of different countries to GPT-3.5’s opinion distribution and the average human opinion distribution alignment scores.

图 16：不同国家分别相对于 GPT-3.5 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 17: The relationship of different countries to GPT-4’s opinion distribution and the average human opinion distribution alignment scores.

图 17：不同国家分别相对于 GPT-4 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 18: The relationship of different countries to GPT-5’s opinion distribution and the average human opinion distribution alignment scores.

图 18：不同国家分别相对于 GPT-5 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 19: The relationship of different countries to DeepSeek-V3’s opinion distribution and the average human opinion distribution alignment scores.

图 19：不同国家分别相对于 DeepSeek-V3 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Figure 20: The relationship of different countries to DeepSeek-R1’s opinion distribution and the average human opinion distribution alignment scores.

图 20：不同国家分别相对于 DeepSeek-R1 意见分布、以及相对于人类平均意见分布的对齐分数关系。

Models Figures Aya23 Figure 13 Qwen2.5 Figure 14 Llama3 Figure 15 GPT-3.5 Figure 16 GPT-4 Figure 17 GPT-5 Figure 18 DeepSeek-V3 Figure 19 DeepSeek-R1 Figure 20 Table 8: Checklist of relationship of different countries to all seven LLMs’ opinion distribution and the average human opinion distribution alignment scores.

Models	Figures
Aya23	Figure 13
Qwen2.5	Figure 14
Llama3	Figure 15
GPT-3.5	Figure 16
GPT-4	Figure 17
GPT-5	Figure 18
DeepSeek-V3	Figure 19
DeepSeek-R1	Figure 20

模型 图 Aya23 图 13 Qwen2.5 图 14 Llama3 图 15 GPT-3.5 图 16 GPT-4 图 17 GPT-5 图 18 DeepSeek-V3 图 19 DeepSeek-R1 图 20 表 8：不同国家分别相对于全部七个模型意见分布、以及相对于人类平均意见分布的对齐分数关系一览表。

模型	图
Aya23	图 13
Qwen2.5	图 14
Llama3	图 15
GPT-3.5	图 16
GPT-4	图 17
GPT-5	图 18
DeepSeek-V3	图 19
DeepSeek-R1	图 20

We find that Aya23, GPT-3.5, and DeepSeek-V3 do not achieve alignment scores higher than the average human alignment with any country. Other LLMs align well with only a few countries, such as the United States and Canada, but show poor alignment with most countries. This indicates that aligning LLMs with human opinions remains a considerable challenge. In addition, it should be noted that the alignment scores of LLMs with countries are positively correlated with the alignment scores of average human distributions with countries; that is, LLMs are more likely to align with countries that have more aligned average human distributions.

我们发现，Aya23、GPT-3.5 和 DeepSeek-V3 与任何国家的对齐分数都没有超过人类平均对齐。其他模型只与美国和加拿大等少数国家对齐较好，而对大多数国家则对齐较差。这表明，让模型与人类意见对齐仍然是一项相当大的挑战。此外需要指出的是，模型与各国的对齐分数，与人类平均分布与各国的对齐分数呈正相关；也就是说，模型更可能与那些人类平均分布对齐程度更高的国家对齐。

Appendix E More Results of RQ2

附录 E RQ2 的更多结果

t

Method	Aya23	Llama3	Qwen2.5	GPT-3.5	GPT-4	GPT-5	DS-V3	DS-R1	AVG.
Japan (Japanese)
No Steering	0.8238	0.8513	0.8596	0.8015	0.8441	0.8744	0.8251	0.8541	0.8417
+Language Steering (Ja.)	0.8400	0.8516	0.8686	0.8141	0.8762∗∗**	0.8945∗**	0.8760∗∗∗**	0.8931∗∗∗**	0.8643∗∗∗**
Persona Steering (En.)	0.8400	0.8566	0.8631	0.8035	0.8603	0.8884	0.8422	0.8680	0.8528∗
+Language Steering (Ja.)	0.8444∗**	0.8540	0.8733	0.8287	0.8899∗∗∗**	0.9123∗∗∗**	0.8847∗∗∗**	0.8996∗∗∗**	0.8734∗∗∗**
Few-shot Steering (En.)	0.8402	0.8618	0.8696	0.8327∗**	0.8757∗∗	0.8920	0.8810∗∗∗	0.8788∗	0.8665∗∗∗
+Language Steering (Ja.)	0.8696∗∗∗**	0.8508	0.8807∗**	0.8280	0.9027∗∗∗**	0.9053∗∗∗**	0.8932∗∗∗**	0.8964∗∗∗**	0.8783∗∗∗**
Korea (Korean)
No Steering (En.)	0.8244	0.8415	0.8632	0.8283	0.8445	0.8659	0.8378	0.8606	0.8458
+Language Steering (Ko.)	0.8230	0.8427	0.8607	0.8379	0.8530	0.8737	0.8685∗**	0.8693	0.8536
Persona Steering (En.)	0.8341	0.8357	0.8640	0.8256	0.8721∗**	0.8814∗	0.8747∗∗	0.8875∗∗	0.8594∗∗∗
+Language Steering (Ko.)	0.8367	0.8479	0.8660	0.8354	0.8682∗	0.8844	0.8786∗∗∗**	0.8932∗∗**	0.8638∗∗∗**
Few-shot Steering (En.)	0.8313	0.8591	0.8646	0.8405	0.8700∗	0.8718	0.8831∗∗∗	0.8637	0.8605∗∗∗
+Language Steering (Ko.)	0.8531∗**	0.8749∗∗**	0.8790	0.8441	0.8857∗∗**	0.8960∗**	0.8953∗∗∗**	0.9019∗∗∗**	0.8788∗∗∗**
Russia (Russian)
No Steering (En.)	0.8421	0.8415	0.8644	0.8414	0.8640	0.8862	0.8569	0.8780	0.8593
+Language Steering (Ru.)	0.8347	0.8682∗**	0.8776	0.8408	0.8977∗∗**	0.9103∗∗**	0.9001∗∗∗**	0.8880	0.8772∗∗∗**
Persona Steering (En.)	0.8495	0.8513	0.8854	0.8578	0.8953∗∗	0.9180∗∗∗**	0.8978∗∗∗	0.9018∗	0.8821∗∗∗
+Language Steering (Ru.)	0.8668∗**	0.8729∗∗**	0.8895∗**	0.8344	0.9002∗∗∗**	0.8976	0.9129∗∗∗**	0.9084∗∗∗**	0.8853∗∗∗**
Few-shot Steering (En.)	0.8562	0.8801∗∗∗	0.8865	0.8700∗**	0.8936∗∗	0.9172∗∗∗	0.9058∗∗∗	0.8939	0.8879∗∗∗
+Language Steering (Ru.)	0.8699∗**	0.8874∗∗∗**	0.8947∗∗**	0.8457	0.9095∗∗∗**	0.9247∗∗∗**	0.9084∗∗∗**	0.8975∗**	0.8922∗∗∗**
Viet Nam (Vietnamese)
No Steering (En.)	0.8105	0.7992	0.8094	0.8019	0.8179	0.8111	0.8078	0.8164	0.8093
+Language Steering (Vi.)	0.8129	0.8364∗∗**	0.8487∗∗**	0.8062	0.8523∗∗**	0.8407∗**	0.8489∗∗∗**	0.8496∗∗**	0.8370∗∗∗**
Persona Steering (En.)	0.8070	0.8078	0.7987	0.7975	0.8235	0.8337	0.8227	0.8253	0.8145
+Language Steering (Vi.)	0.8100	0.8382∗∗**	0.8408∗**	0.8114	0.8612∗∗∗**	0.8544∗∗∗**	0.8481∗∗**	0.8525∗∗**	0.8396∗∗∗**
Few-shot Steering (En.)	0.7961	0.8498∗∗∗	0.8190	0.8218	0.8470∗	0.8450∗	0.8444∗	0.8396	0.8328∗∗∗
+Language Steering (Vi.)	0.8443∗∗**	0.8668∗∗∗**	0.8718∗∗∗**	0.8411∗∗**	0.8808∗∗∗**	0.8713∗∗∗**	0.8854∗∗∗**	0.8674∗∗∗**	0.8661∗∗∗**
Brazil (Portuguese)
No Steering (En.)	0.8294	0.8455	0.8693	0.8288	0.8606	0.8834	0.8529	0.8655	0.8544
+Language Steering (Pt.)	0.8450	0.8557	0.8791	0.8453	0.8914∗∗∗**	0.8753	0.8882∗∗∗**	0.8828	0.8704∗∗∗**
Persona Steering (En.)	0.8583∗**	0.8380	0.8820	0.8422	0.8855∗∗	0.8983	0.8792∗∗	0.8815	0.8706∗∗∗
+Language Steering (Pt.)	0.8552	0.8680∗**	0.8885∗**	0.8489	0.8992∗∗∗**	0.8878	0.8945∗∗∗**	0.8872∗**	0.8787∗∗∗**
Few-shot Steering (En.)	0.8518	0.8777∗∗**	0.8842	0.8696∗∗∗	0.8879∗∗	0.9047∗	0.8964∗∗∗	0.8805	0.8816∗∗∗
+Language Steering (Pt.)	0.8569∗**	0.8755∗∗	0.8869	0.8720∗∗∗**	0.8995∗∗∗**	0.9049∗∗**	0.8987∗∗∗**	0.8939∗**	0.8860∗∗∗**
Argentina (Spanish)
No Steering (En.)	0.8392	0.8397	0.8698	0.8310	0.8694	0.8943	0.8579	0.8766	0.8597
+Language Steering (Es.)	0.8349	0.8518	0.8764	0.8138	0.8890∗**	0.8953	0.8868∗∗**	0.8912	0.8674
Persona Steering (En.)	0.8294	0.8429	0.8774	0.8248	0.8869	0.9101	0.8804∗	0.8929	0.8681∗
+Language Steering (Es.)	0.8540	0.8628	0.8874∗**	0.8303	0.8896∗**	0.9063	0.8921∗∗∗**	0.8980∗**	0.8776∗∗∗**
Few-shot Steering (En.)	0.8612	0.8627	0.8984∗**	0.8483∗∗**	0.9021∗∗∗**	0.9153∗∗**	0.9052∗∗∗	0.8947	0.8860∗∗∗**
+Language Steering (Es.)	0.8546	0.8684	0.8983∗∗	0.8267∗∗	0.8999∗∗	0.9080	0.9099∗∗∗**	0.8985∗**	0.8830∗∗∗
Chile (Spanish)
No Steering (En.)	0.8627	0.8728	0.8936	0.8570	0.8830	0.9051	0.8798	0.8937	0.8810
+Language Steering (Es.)	0.8508	0.8824	0.8985	0.8444	0.9074∗∗**	0.9044	0.9076∗∗**	0.9033	0.8874
Persona Steering (En.)	0.8561	0.8836	0.9040	0.8486	0.9090∗∗	0.9152	0.9054∗∗	0.9091	0.8914∗∗
+Language Steering (Es.)	0.8634	0.8929∗**	0.9047	0.8556	0.9120∗∗∗**	0.9132	0.9111∗∗∗**	0.9012	0.8943∗∗∗**
Few-shot Steering (En.)	0.8757	0.8987∗∗	0.9075	0.8607	0.9141∗∗∗	0.9230∗**	0.9150∗∗∗	0.8989	0.8992∗∗∗
+Language Steering (Es.)	0.8660	0.9019∗∗∗**	0.9020	0.8569	0.9177∗∗∗**	0.9210∗	0.9198∗∗∗**	0.9116∗**	0.8996∗∗∗**
Uruguay (Spanish)
No Steering (En.)	0.8475	0.8607	0.8766	0.8322	0.8767	0.8893	0.8595	0.8639	0.8633
+Language Steering (Es.)	0.8389	0.8592	0.8744	0.8222	0.8858	0.8831	0.8769	0.8711	0.8640
Persona Steering (En.)	0.8466	0.8584	0.8797	0.8219	0.8742	0.8946	0.8810∗**	0.8900∗∗**	0.8683
+Language Steering (Es.)	0.8534	0.8648	0.8802	0.8345	0.8823	0.8950	0.8797∗	0.8898∗∗	0.8725∗∗**
Few-shot Steering (En.)	0.8564	0.8642	0.8849	0.8267	0.8853	0.9050	0.8850∗∗	0.8804	0.8735∗∗
+Language Steering (Es.)	0.8513	0.8709	0.8899	0.8286	0.8921	0.8989	0.8854∗∗**	0.8923∗∗∗**	0.8762∗∗∗**

t

方法	Aya23	Llama3	Qwen2.5	GPT-3.5	GPT-4	GPT-5	DS-V3	DS-R1	平均
日本（日语）
无引导	0.8238	0.8513	0.8596	0.8015	0.8441	0.8744	0.8251	0.8541	0.8417
+语言引导（日）	0.8400	0.8516	0.8686	0.8141	0.8762∗∗**	0.8945∗**	0.8760∗∗∗**	0.8931∗∗∗**	0.8643∗∗∗**
人格引导（英）	0.8400	0.8566	0.8631	0.8035	0.8603	0.8884	0.8422	0.8680	0.8528∗
+语言引导（日）	0.8444∗**	0.8540	0.8733	0.8287	0.8899∗∗∗**	0.9123∗∗∗**	0.8847∗∗∗**	0.8996∗∗∗**	0.8734∗∗∗**
少样本引导（英）	0.8402	0.8618	0.8696	0.8327∗**	0.8757∗∗	0.8920	0.8810∗∗∗	0.8788∗	0.8665∗∗∗
+语言引导（日）	0.8696∗∗∗**	0.8508	0.8807∗**	0.8280	0.9027∗∗∗**	0.9053∗∗∗**	0.8932∗∗∗**	0.8964∗∗∗**	0.8783∗∗∗**
韩国（韩语）
无引导（英）	0.8244	0.8415	0.8632	0.8283	0.8445	0.8659	0.8378	0.8606	0.8458
+语言引导（韩）	0.8230	0.8427	0.8607	0.8379	0.8530	0.8737	0.8685∗**	0.8693	0.8536
人格引导（英）	0.8341	0.8357	0.8640	0.8256	0.8721∗**	0.8814∗	0.8747∗∗	0.8875∗∗	0.8594∗∗∗
+语言引导（韩）	0.8367	0.8479	0.8660	0.8354	0.8682∗	0.8844	0.8786∗∗∗**	0.8932∗∗**	0.8638∗∗∗**
少样本引导（英）	0.8313	0.8591	0.8646	0.8405	0.8700∗	0.8718	0.8831∗∗∗	0.8637	0.8605∗∗∗
+语言引导（韩）	0.8531∗**	0.8749∗∗**	0.8790	0.8441	0.8857∗∗**	0.8960∗**	0.8953∗∗∗**	0.9019∗∗∗**	0.8788∗∗∗**
俄罗斯（俄语）
无引导（英）	0.8421	0.8415	0.8644	0.8414	0.8640	0.8862	0.8569	0.8780	0.8593
+语言引导（俄）	0.8347	0.8682∗**	0.8776	0.8408	0.8977∗∗**	0.9103∗∗**	0.9001∗∗∗**	0.8880	0.8772∗∗∗**
人格引导（英）	0.8495	0.8513	0.8854	0.8578	0.8953∗∗	0.9180∗∗∗**	0.8978∗∗∗	0.9018∗	0.8821∗∗∗
+语言引导（俄）	0.8668∗**	0.8729∗∗**	0.8895∗**	0.8344	0.9002∗∗∗**	0.8976	0.9129∗∗∗**	0.9084∗∗∗**	0.8853∗∗∗**
少样本引导（英）	0.8562	0.8801∗∗∗	0.8865	0.8700∗**	0.8936∗∗	0.9172∗∗∗	0.9058∗∗∗	0.8939	0.8879∗∗∗
+语言引导（俄）	0.8699∗**	0.8874∗∗∗**	0.8947∗∗**	0.8457	0.9095∗∗∗**	0.9247∗∗∗**	0.9084∗∗∗**	0.8975∗**	0.8922∗∗∗**
越南（越南语）
无引导（英）	0.8105	0.7992	0.8094	0.8019	0.8179	0.8111	0.8078	0.8164	0.8093
+语言引导（越）	0.8129	0.8364∗∗**	0.8487∗∗**	0.8062	0.8523∗∗**	0.8407∗**	0.8489∗∗∗**	0.8496∗∗**	0.8370∗∗∗**
人格引导（英）	0.8070	0.8078	0.7987	0.7975	0.8235	0.8337	0.8227	0.8253	0.8145
+语言引导（越）	0.8100	0.8382∗∗**	0.8408∗**	0.8114	0.8612∗∗∗**	0.8544∗∗∗**	0.8481∗∗**	0.8525∗∗**	0.8396∗∗∗**
少样本引导（英）	0.7961	0.8498∗∗∗	0.8190	0.8218	0.8470∗	0.8450∗	0.8444∗	0.8396	0.8328∗∗∗
+语言引导（越）	0.8443∗∗**	0.8668∗∗∗**	0.8718∗∗∗**	0.8411∗∗**	0.8808∗∗∗**	0.8713∗∗∗**	0.8854∗∗∗**	0.8674∗∗∗**	0.8661∗∗∗**
巴西（葡萄牙语）
无引导（英）	0.8294	0.8455	0.8693	0.8288	0.8606	0.8834	0.8529	0.8655	0.8544
+语言引导（葡）	0.8450	0.8557	0.8791	0.8453	0.8914∗∗∗**	0.8753	0.8882∗∗∗**	0.8828	0.8704∗∗∗**
人格引导（英）	0.8583∗**	0.8380	0.8820	0.8422	0.8855∗∗	0.8983	0.8792∗∗	0.8815	0.8706∗∗∗
+语言引导（葡）	0.8552	0.8680∗**	0.8885∗**	0.8489	0.8992∗∗∗**	0.8878	0.8945∗∗∗**	0.8872∗**	0.8787∗∗∗**
少样本引导（英）	0.8518	0.8777∗∗**	0.8842	0.8696∗∗∗	0.8879∗∗	0.9047∗	0.8964∗∗∗	0.8805	0.8816∗∗∗
+语言引导（葡）	0.8569∗**	0.8755∗∗	0.8869	0.8720∗∗∗**	0.8995∗∗∗**	0.9049∗∗**	0.8987∗∗∗**	0.8939∗**	0.8860∗∗∗**
阿根廷（西班牙语）
无引导（英）	0.8392	0.8397	0.8698	0.8310	0.8694	0.8943	0.8579	0.8766	0.8597
+语言引导（西）	0.8349	0.8518	0.8764	0.8138	0.8890∗**	0.8953	0.8868∗∗**	0.8912	0.8674
人格引导（英）	0.8294	0.8429	0.8774	0.8248	0.8869	0.9101	0.8804∗	0.8929	0.8681∗
+语言引导（西）	0.8540	0.8628	0.8874∗**	0.8303	0.8896∗**	0.9063	0.8921∗∗∗**	0.8980∗**	0.8776∗∗∗**
少样本引导（英）	0.8612	0.8627	0.8984∗**	0.8483∗∗**	0.9021∗∗∗**	0.9153∗∗**	0.9052∗∗∗	0.8947	0.8860∗∗∗**
+语言引导（西）	0.8546	0.8684	0.8983∗∗	0.8267∗∗	0.8999∗∗	0.9080	0.9099∗∗∗**	0.8985∗**	0.8830∗∗∗
智利（西班牙语）
无引导（英）	0.8627	0.8728	0.8936	0.8570	0.8830	0.9051	0.8798	0.8937	0.8810
+语言引导（西）	0.8508	0.8824	0.8985	0.8444	0.9074∗∗**	0.9044	0.9076∗∗**	0.9033	0.8874
人格引导（英）	0.8561	0.8836	0.9040	0.8486	0.9090∗∗	0.9152	0.9054∗∗	0.9091	0.8914∗∗
+语言引导（西）	0.8634	0.8929∗**	0.9047	0.8556	0.9120∗∗∗**	0.9132	0.9111∗∗∗**	0.9012	0.8943∗∗∗**
少样本引导（英）	0.8757	0.8987∗∗	0.9075	0.8607	0.9141∗∗∗	0.9230∗**	0.9150∗∗∗	0.8989	0.8992∗∗∗
+语言引导（西）	0.8660	0.9019∗∗∗**	0.9020	0.8569	0.9177∗∗∗**	0.9210∗	0.9198∗∗∗**	0.9116∗**	0.8996∗∗∗**
乌拉圭（西班牙语）
无引导（英）	0.8475	0.8607	0.8766	0.8322	0.8767	0.8893	0.8595	0.8639	0.8633
+语言引导（西）	0.8389	0.8592	0.8744	0.8222	0.8858	0.8831	0.8769	0.8711	0.8640
人格引导（英）	0.8466	0.8584	0.8797	0.8219	0.8742	0.8946	0.8810∗**	0.8900∗∗**	0.8683
+语言引导（西）	0.8534	0.8648	0.8802	0.8345	0.8823	0.8950	0.8797∗	0.8898∗∗	0.8725∗∗**
少样本引导（英）	0.8564	0.8642	0.8849	0.8267	0.8853	0.9050	0.8850∗∗	0.8804	0.8735∗∗
+语言引导（西）	0.8513	0.8709	0.8899	0.8286	0.8921	0.8989	0.8854∗∗**	0.8923∗∗∗**	0.8762∗∗∗**

In addition to the Chinese and German in the main text, we provide the experimental results of Japanese, Korean, Russian, Vietnamese, Portuguese, and Spanish in Table 9. The experimental results show that language steering is more effective when a language is used by a single country. However, when a language such as Spanish is used by many countries, language steering may cause LLMs to express the average opinion of Spanish-speaking countries rather than the opinion of a specific country. This result suggests that when steering LLMs to emulate the opinions of these countries, features other than language should also be considered.

除了正文中的中文和德语之外，我们在表 9 中给出日语、韩语、俄语、越南语、葡萄牙语和西班牙语的实验结果。实验结果表明，当一种语言只被单一国家使用时，语言引导更为有效。然而，当一种语言（例如西班牙语）被许多国家使用时，语言引导可能使模型表达出西班牙语国家的平均意见，而不是某一特定国家的意见。这一结果说明，在引导模型去模仿这些国家的意见时，还应当考虑语言以外的特征。

Appendix F More Results of RQ3

附录 F RQ3 的更多结果

\tau=0.02

\tau=0.02

Models Countries (or Regions) Qwen2.5 Australia, Germany, Japan, Netherlands, New Zealand, South Korea, United States, Uruguay Llama3 Australia, Germany, Japan, Netherlands, New Zealand GPT-4 Australia, Germany, Netherlands, New Zealand, United States, Uruguay GPT-5 Argentina, Hong Kong, Japan, South Korea, Taiwan, Uruguay DeepSeek-R1 Australia, Germany, Hong Kong, Japan, Netherlands, New Zealand, United States Table 10: Filtered countries for each LLM.

Models	Countries (or Regions)
Qwen2.5	Australia, Germany, Japan, Netherlands, New Zealand, South Korea, United States, Uruguay
Llama3	Australia, Germany, Japan, Netherlands, New Zealand
GPT-4	Australia, Germany, Netherlands, New Zealand, United States, Uruguay
GPT-5	Argentina, Hong Kong, Japan, South Korea, Taiwan, Uruguay
DeepSeek-R1	Australia, Germany, Hong Kong, Japan, Netherlands, New Zealand, United States

模型 国家（或地区） Qwen2.5 澳大利亚、德国、日本、荷兰、新西兰、韩国、美国、乌拉圭 Llama3 澳大利亚、德国、日本、荷兰、新西兰 GPT-4 澳大利亚、德国、荷兰、新西兰、美国、乌拉圭 GPT-5 阿根廷、香港、日本、韩国、台湾、乌拉圭 DeepSeek-R1 澳大利亚、德国、香港、日本、荷兰、新西兰、美国 表 10：为每个模型筛选出的国家。

模型	国家（或地区）
Qwen2.5	澳大利亚、德国、日本、荷兰、新西兰、韩国、美国、乌拉圭
Llama3	澳大利亚、德国、日本、荷兰、新西兰
GPT-4	澳大利亚、德国、荷兰、新西兰、美国、乌拉圭
GPT-5	阿根廷、香港、日本、韩国、台湾、乌拉圭
DeepSeek-R1	澳大利亚、德国、香港、日本、荷兰、新西兰、美国

Figure 21: The trend of the average alignment scores of LLMs with countries across waves. The sahed area indicates the standard deviation of the alignment scores at the current wave.

图 21：各模型跨各波次与各国平均对齐分数的趋势。阴影区域表示当前波次对齐分数的标准差。

Appendix G More Results of Discussion

附录 G 讨论的更多结果

G.1 Alignment of Human Opinions across Countries

G.1 各国人类意见之间的对齐

We provide a full version of the alignment score heatmap between countries and LLMs in Figure 22.

我们在图 22 中给出国家与模型之间对齐分数热图的完整版本。

Figure 22: A full version of the alignment score heatmap between countries and LLMs. The source code for plotting this figure is located in the show_results folder.

图 22：国家与模型之间对齐分数热图的完整版本。绘制本图的源代码位于 show_results 文件夹。

Individual columns and rows appear as “darker horizontal and vertical stripes”: this means that some countries are less aligned with most other countries (systematic outliers). There are several noticeable dark stripes in the figure, such as Northern Ireland, the Netherlands, and New Zealand. However, these countries have high alignment scores with each other and with Australia, Brazil, and Great Britain. The alignment scores between LLMs are relatively high, indicating a certain degree of homogeneity between the LLMs. This may be because they have similar training corpora Bommasani et al. (2022). We also find that LLMs consistently exhibit poor alignment with certain countries. For example, Bangladesh, Egypt, Iraq, Libya, and Myanmar, etc. These findings supplement the conclusions in the main text.

个别行和列呈现为“颜色更深的横条和竖条”：这意味着某些国家与大多数其他国家的对齐较弱（系统性离群点）。图中有几条明显的深色条带，例如北爱尔兰、荷兰和新西兰。不过，这些国家彼此之间，以及与澳大利亚、巴西和大不列颠之间的对齐分数较高。各模型之间的对齐分数相对较高，说明模型之间存在一定程度的同质性。这可能是因为它们具有相似的训练语料 Bommasani et al. (2022)。我们还发现，模型始终与某些国家对齐较差。例如孟加拉国、埃及、伊拉克、利比亚和缅甸等。这些发现补充了正文中的结论。

G.2 Internal Consistency of LLMs

G.2 大语言模型的内部一致性

Topic Question Options Gender Fairness Q29: Do you agree that, on the whole, men make better political leaders than women do? 1. Strongly agree 2. Agree 3. Disagree 4. Strongly disagree Q30: Do you agree that a university education is more important for a boy than for a girl? 1. Strongly agree 2. Agree 3. Disagree 4. Strongly disagree Q31: Do you agree that, on the whole, men make better business executives than women do? 1. Strongly agree 2. Agree 3. Disagree 4. Strongly disagree Q33: Do you agree that when jobs are scarce, men should have more right to a job than women? 1. Strongly agree 2. Agree 3. Neither agree nor disagree 4. Disagree 5. Strongly disagree Atheism Q165: Do you believe in God? 1. Yes 2. No Q166: Do you believe in life after death? 1. Yes 2. No Q167: Do you believe in hell? 1. Yes 2. No Q168: Do you believe in heaven? 1. Yes 2. No Democracy Q243: How essential do you think it is as a characteristic of democracy that people choose their leaders in free elections? 1. Not an essential characteristic of democracy 10. An essential characteristic of democracy Q250: How important is it for you to live in a country that is governed democratically? 1. Not at all important 10. Absolutely important Table 11: The questions we selected for studying the internal consistency of LLMs.

Topic	Question	Options
Gender Fairness	Q29: Do you agree that, on the whole, men make better political leaders than women do?	1. Strongly agree 2. Agree 3. Disagree 4. Strongly disagree
	Q30: Do you agree that a university education is more important for a boy than for a girl?	1. Strongly agree 2. Agree 3. Disagree 4. Strongly disagree
	Q31: Do you agree that, on the whole, men make better business executives than women do?	1. Strongly agree 2. Agree 3. Disagree 4. Strongly disagree
	Q33: Do you agree that when jobs are scarce, men should have more right to a job than women?	1. Strongly agree 2. Agree 3. Neither agree nor disagree 4. Disagree 5. Strongly disagree
Atheism	Q165: Do you believe in God?	1. Yes 2. No
	Q166: Do you believe in life after death?	1. Yes 2. No
	Q167: Do you believe in hell?	1. Yes 2. No
	Q168: Do you believe in heaven?	1. Yes 2. No
Democracy	Q243: How essential do you think it is as a characteristic of democracy that people choose their leaders in free elections?	1. Not an essential characteristic of democracy 10. An essential characteristic of democracy
	Q250: How important is it for you to live in a country that is governed democratically?	1. Not at all important 10. Absolutely important

主题 题目 选项 性别公平 Q29：你是否同意，总体而言，男性比女性更适合担任政治领导人？ 1. 非常同意 2. 同意 3. 不同意 4. 非常不同意 Q30：你是否同意，大学教育对男孩比对女孩更重要？ 1. 非常同意 2. 同意 3. 不同意 4. 非常不同意 Q31：你是否同意，总体而言，男性比女性更适合担任企业高管？ 1. 非常同意 2. 同意 3. 不同意 4. 非常不同意 Q33：你是否同意，当工作岗位短缺时，男性应当比女性更有权利获得一份工作？ 1. 非常同意 2. 同意 3. 既不同意也不反对 4. 不同意 5. 非常不同意 无神论 Q165：你是否相信上帝？ 1. 是 2. 否 Q166：你是否相信死后的生命？ 1. 是 2. 否 Q167：你是否相信地狱？ 1. 是 2. 否 Q168：你是否相信天堂？ 1. 是 2. 否 民主 Q243：你认为，“人民在自由选举中选择领导人”作为民主的一项特征有多必不可少？ 1. 不是民主的必要特征 10. 是民主的必要特征 Q250：生活在一个以民主方式治理的国家，对你来说有多重要？ 1. 完全不重要 10. 绝对重要 表 11：我们为研究模型内部一致性所选出的题目。

主题	题目	选项
性别公平	Q29：你是否同意，总体而言，男性比女性更适合担任政治领导人？	1. 非常同意 2. 同意 3. 不同意 4. 非常不同意
	Q30：你是否同意，大学教育对男孩比对女孩更重要？	1. 非常同意 2. 同意 3. 不同意 4. 非常不同意
	Q31：你是否同意，总体而言，男性比女性更适合担任企业高管？	1. 非常同意 2. 同意 3. 不同意 4. 非常不同意
	Q33：你是否同意，当工作岗位短缺时，男性应当比女性更有权利获得一份工作？	1. 非常同意 2. 同意 3. 既不同意也不反对 4. 不同意 5. 非常不同意
无神论	Q165：你是否相信上帝？	1. 是 2. 否
	Q166：你是否相信死后的生命？	1. 是 2. 否
	Q167：你是否相信地狱？	1. 是 2. 否
	Q168：你是否相信天堂？	1. 是 2. 否
民主	Q243：你认为，“人民在自由选举中选择领导人”作为民主的一项特征有多必不可少？	1. 不是民主的必要特征 10. 是民主的必要特征
	Q250：生活在一个以民主方式治理的国家，对你来说有多重要？	1. 完全不重要 10. 绝对重要

We provide a list of questions we selected for studying the internal consistency of LLMs in Table 11.

我们在表 11 中给出为研究模型内部一致性所选出的题目列表。

G.3 Sensitivity of LLMs

G.3 大语言模型的敏感性

Inspired by Santurkar et al. (2023), we test the prompt sensitivity of the LLMs used in our experiments against the following factors:

受 Santurkar et al. (2023) 的启发，我们针对下列因素测试了本实验所用模型的提示敏感性：

• The order in which the question options are presented to the model. The file dataset/questions/WV7_shuffle.jsonl contains data with the order of options shuffled.

• 向模型呈现问题选项的 次序 。文件 dataset/questions/WV7_shuffle.jsonl 包含选项次序被打乱后的数据。

• The few-shot examples (the number of the few-shot examples and the probability distribution of the few-shot examples). The file inputs/sensitivity/number.txt is the examples that change the number of few-shot examples from 5 to 3. The file inputs/sensitivity/prob.txt is the examples that change the probability distribution of few-shot examples.

• 少样本示例（少样本示例的 数量 ，以及少样本示例的 概率分布 ）。文件 inputs/sensitivity/number.txt 是把少样本示例数量从 5 改为 3 的示例。文件 inputs/sensitivity/prob.txt 是改变少样本示例概率分布的示例。

All results are listed under the results/Discussion/sensitivity folder.

全部结果列在 results/Discussion/sensitivity 文件夹下。

G.4 The Impact of Model Size

G.4 模型规模的影响

To investigate the impact of different model sizes on experimental results, we evaluated 5 smaller Qwen2.5 (0.5B/1.5B/3B/7B/14B) models. Figure 23 shows the alignment scores of the Qwen2.5 model in different sizes for each country. Although previous studies have shown that model alignment is not directly related to model size (Liu and Chu 2025). In Figure 23, we can observe that, except for Qwen2.5-1.5B-Instruct, the alignment score exhibits a clear positive correlation with model size, confirming that larger models more accurately capture the distribution of human values. We tests the language steering performance on Chinese and German across different size Qwen models. The experimental results are shown in Table 12. Language steering also performed more effectively on larger models.

为了考察不同模型规模对实验结果的影响，我们评估了 5 个更小的 Qwen2.5（0.5B/1.5B/3B/7B/14B）模型。图 23 给出了不同规模的 Qwen2.5 模型与每个国家的对齐分数。尽管先前研究已经表明，模型对齐与模型规模并无直接关系 (Liu and Chu 2025)。在图 23 中可以看到，除了 Qwen2.5-1.5B-Instruct 之外，对齐分数与模型规模呈现出明显的正相关，这证实更大规模的模型能更准确地捕捉人类价值观的分布。我们在不同规模的 Qwen 模型上测试了中文和德语的语言引导表现。实验结果如表 12 所示。语言引导在更大规模的模型上同样更为有效。

Figure 23: The alignment score of Qwen 2.5 models of different sizes with each country, where each point represents a country and the black line represents the average alignment score of all countries.

图 23：不同规模的 Qwen 2.5 模型与每个国家的对齐分数，每个点代表一个国家，黑线代表所有国家的平均对齐分数。

t

Method	0.5B	1.5B	3B	7B	14B	72B	AVG.
China
No Steering (En.)	0.7193	0.7569	0.7614	0.7960	0.8156	0.8273	0.7794
+Language Steering (Zh.)	0.7059	0.7427	0.7723	0.8173	0.8428	0.8439	0.7875
Persona Steering (En.)	0.7211	0.7627	0.7569	0.7990	0.8249	0.8316	0.7827
+Language Steering (Zh.)	0.7024	0.7560	0.7736	0.8183	0.8586∗∗**	0.8629∗**	0.7953∗**
Few-shot Steering (En.)	0.7341	0.7808	0.8072∗∗**	0.8143	0.8493	0.8596	0.8076∗∗∗
+Language Steering (Zh.)	0.7346	0.7751	0.7934	0.8357∗**	0.8754∗∗∗**	0.8838∗∗∗**	0.8163∗∗∗**
Germany
No Steering (En.)	0.7356	0.7680	0.7398	0.8227	0.8597	0.8541	0.7967
+Language Steering (De.)	0.7331	0.7620	0.7761	0.8022	0.8469	0.8815∗∗**	0.8803
Persona Steering (En.)	0.7416	0.7757	0.7418	0.8244	0.8672	0.8596	0.8217
+Language Steering (De.)	0.7431	0.7630	0.7898∗∗**	0.8086	0.8552	0.8904∗∗∗**	0.8084
Few-shot Steering (En.)	0.7189	0.7531	0.7730	0.8200	0.8534	0.8619	0.7967
+Language Steering (De.)	0.7509	0.7605	0.7808∗**	0.8200	0.8746	0.8916∗∗∗**	0.8131∗**

t

方法	0.5B	1.5B	3B	7B	14B	72B	平均
中国
无引导（英）	0.7193	0.7569	0.7614	0.7960	0.8156	0.8273	0.7794
+语言引导（中）	0.7059	0.7427	0.7723	0.8173	0.8428	0.8439	0.7875
人格引导（英）	0.7211	0.7627	0.7569	0.7990	0.8249	0.8316	0.7827
+语言引导（中）	0.7024	0.7560	0.7736	0.8183	0.8586∗∗**	0.8629∗**	0.7953∗**
少样本引导（英）	0.7341	0.7808	0.8072∗∗**	0.8143	0.8493	0.8596	0.8076∗∗∗
+语言引导（中）	0.7346	0.7751	0.7934	0.8357∗**	0.8754∗∗∗**	0.8838∗∗∗**	0.8163∗∗∗**
德国
无引导（英）	0.7356	0.7680	0.7398	0.8227	0.8597	0.8541	0.7967
+语言引导（德）	0.7331	0.7620	0.7761	0.8022	0.8469	0.8815∗∗**	0.8803
人格引导（英）	0.7416	0.7757	0.7418	0.8244	0.8672	0.8596	0.8217
+语言引导（德）	0.7431	0.7630	0.7898∗∗**	0.8086	0.8552	0.8904∗∗∗**	0.8084
少样本引导（英）	0.7189	0.7531	0.7730	0.8200	0.8534	0.8619	0.7967
+语言引导（德）	0.7509	0.7605	0.7808∗**	0.8200	0.8746	0.8916∗∗∗**	0.8131∗**

G.5 Other Matrics

G.5 其他指标

r

alignment

r

	Alignment	NMAE	JSD
Alignment	1	0.9150	0.8757
NMAE		1	0.9390
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9150	0.8757
NMAE		1	0.9390
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.8696	0.8584
NMAE		1	0.8920
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.8696	0.8584
NMAE		1	0.8920
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9102	0.9040
NMAE		1	0.8921
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9102	0.9040
NMAE		1	0.8921
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9287	0.9259
NMAE		1	0.8751
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9287	0.9259
NMAE		1	0.8751
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9125	0.9113
NMAE		1	0.9268
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9125	0.9113
NMAE		1	0.9268
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9244	0.9294
NMAE		1	0.9362
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9244	0.9294
NMAE		1	0.9362
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9094	0.9195
NMAE		1	0.8975
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.9094	0.9195
NMAE		1	0.8975
JS 散度			1

r

	Alignment	NMAE	JSD
Alignment	1	0.8898	0.9245
NMAE		1	0.9004
JS div			1

r

	Alignment	NMAE	JSD
Alignment	1	0.8898	0.9245
NMAE		1	0.9004
JS 散度			1

G.6 Other Baselines

G.6 其他基线

Take DeepSeek-R1 as an example, we also perform a religious analysis, using religion as a baseline. As shown in Figure 24, most religions are under-aligned except Jew, which is appropriately aligned. In addition, as shown in Figures 25 and 26, we include regional (e.g., North American and Europe) averages to enrich our baseline. For example, within the North American region, Canada is over-aligned, the US is appropriately aligned, while Mexico, Puerto Rico, and Guatemala are under-aligned.

以 DeepSeek-R1 为例，我们还做了一项宗教分析，把宗教作为基线。如图 24 所示，除犹太教对齐得当之外，大多数宗教都对准不足。此外，如图 25 和图 26 所示，我们纳入地区（例如北美和欧洲）平均水平，以丰富基线。例如，在北美地区内，加拿大被过度对齐，美国对齐得当，而墨西哥、波多黎各和危地马拉则对准不足。

Figure 24: Religion as the baseline.

图 24：以宗教为基线。

Figure 25: Region (America) as the baseline.

图 25：以地区（美洲）为基线。

Figure 26: Region (Europe) as the baseline.

图 26：以地区（欧洲）为基线。

Appendix H Limitations

附录 H 局限

We acknowledge that our study has limitations, one of which is that it is limited by the scope of the WVS and cannot cover all countries. The second limitation is that, due to human resource constraints, we cannot examine all languages supported by the WVS. However, we are fortunate to have covered eight languages (Spanish, Chinese, Japanese, Korean, German, Russian, Vietnamese, and Portuguese) and report exciting findings. Another limitation is that we did not consider whether there were cross-linguistic effects if a country conducted its survey using a multilingual WVS questionnaire. At the very least, we hope that these limitations can serve as directions for future studies.

我们承认本研究存在局限，其中之一是它受 WVS 覆盖范围的限制，无法覆盖所有国家。第二个局限是，由于人力限制，我们无法考察 WVS 所支持的全部语言。不过，我们有幸覆盖了八种语言（西班牙语、中文、日语、韩语、德语、俄语、越南语和葡萄牙语），并报告了令人振奋的发现。另一个局限是，如果某个国家使用多语版本的 WVS 问卷开展调查，我们没有考虑其中是否存在跨语言效应。至少，我们希望这些局限能够成为后续研究的方向。

On the Alignment of Large Language Models with Global Human Opinion

大语言模型与全球人类意见的对齐

Abstract

摘要

1 Introduction

1 引言

2 The World Values Survey

2 世界价值观调查

Global Dimension

全球维度

Language Dimension

语言维度

Temporal Dimension

时间维度

3 Experiment Settings

3 实验设置

3.1 Dataset

3.1 数据集

3.2 Models

3.2 模型

3.3 Prompts

3.3 提示

3.4 Distribution Expression Methods

3.4 分布表达方法

LLMs’ opinion distribution

大语言模型的意见分布

Human opinion distribution

人类意见分布

3.5 Alignment Metrics

3.5 对齐指标

4 Global Opinion Alignment (RQ1)

4 全球意见对齐（RQ1）

4.1 Model Level

4.1 模型层面

4.2 Country Level

4.2 国家层面

4.3 Alignment Difference Level

4.3 对齐差异层面

5 Steerability of Language (RQ2)

5 语言的可引导性（RQ2）

Baselines

基线

Method

方法

Effectiveness of Steering

引导的有效性

6 Temporal Opinion Alignment (RQ3)

6 时间维度上的意见对齐（RQ3）

Method

方法

Results

结果

7 Discussion

7 讨论

7.1 Alignment of Human Opinions across Countries

7.1 各国人类意见之间的对齐

7.2 Internal Consistency of LLMs

7.2 大语言模型的内部一致性

7.3 Sensitivity of LLMs

7.3 大语言模型的敏感性

8 Related Work

8 相关工作

Human Opinion and Culture.

人类意见与文化。

Culture in LLMs.

大语言模型中的文化。

Opinion of LLMs.

大语言模型的意见。

Steering Methods.

引导方法。

9 Conclusion

9 结论

Acknowledgments

致谢

References

参考文献

Appendix A More Details of The World Values Survey

附录 A 世界价值观调查的更多细节

Appendix B Experiment Settings

附录 B 实验设置