# On the Alignment of Large Language Models with Global Human Opinion

arXiv: https://arxiv.org/abs/2509.01418 · HTML: https://mlsanity.com/paper/2509.01418/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Large language models that replace human participants can harmfully misportray and flatten identity groups](https://mlsanity.com/paper/2402.01908/) (2024)
- [An Evaluation of Cultural Value Alignment in LLM](https://mlsanity.com/paper/2504.08863/) (2025)
- [Robust Evaluation Measures for Evaluating Social Biases in Masked Language Models](https://mlsanity.com/paper/2401.11601/) (2024)
- [Evaluating Steering Techniques using Human Similarity Judgments](https://mlsanity.com/paper/2505.19333/) (2025)

## Built on by (most cited first, 13 of 13)

- [Understanding the Prompt Sensitivity](https://mlsanity.com/paper/2604.18389/) (2026)
- [Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook](https://mlsanity.com/paper/2604.06210/) (2026)
- [Sentence-Level Contextual Entrainment in Large Language Models](https://mlsanity.com/paper/2606.24077/) (2026)
- [LKValues: Aligning Large Language Models with Sri Lankan Societal Values](https://mlsanity.com/paper/2607.20410/) (2026)
- [Personalization, Personas, and Forecasting in Value Alignment](https://mlsanity.com/paper/2607.24782/) (2026)
- [People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe](https://mlsanity.com/paper/2608.07367/) (2026)
- [Accurate in space, unreliable in time: how LLMs represent national cultural change](https://mlsanity.com/paper/2609.01902/) (2026)
- [More Than 1v1: Human-AI Alignment in Early Developmental Communities with Multimodal LLMs](https://mlsanity.com/paper/2603.07134/) (2026)
- [The Third Ambition: Artificial Intelligence and the Science of Human Behavior](https://mlsanity.com/paper/2603.07329/) (2026)
- [Coherence Maximization Improves Pluralistic Alignment](https://mlsanity.com/paper/2606.03110/) (2026)
- [Belief Cascades Drive Persuasion in LLM Agent Networks](https://mlsanity.com/paper/2608.25152/) (2026)
- [Cultural Misalignment in Large Language Models: Detection, Measurement, and Mitigation Through Targeted Fine-Tuning](https://mlsanity.com/paper/2609.04485/) (2026)
- [ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models](https://mlsanity.com/paper/2610.20472/) (2026)

## Abstract (arXiv)

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.