# Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment

arXiv: https://arxiv.org/abs/2604.12851 · HTML: https://mlsanity.com/paper/2604.12851/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2026.

## Built on by (most cited first, 5 of 5)

- [LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values](https://mlsanity.com/paper/2606.13944/) (2026)
- [The conditional superiority of fast silicon sampling](https://mlsanity.com/paper/2608.14079/) (2026)
- [Signal or Spurious Cue? A Randomized Audit of Survey-Country Metadata in LLM Social Inference](https://mlsanity.com/paper/2608.06085/) (2026)
- [CultureConverse: A Multilingual Multi-turn Simulation Harness for Culturally Grounded Assistance in East and Southeast Asia](https://mlsanity.com/paper/2608.28405/) (2026)
- [Large Language Models Substantially Compress Well-Being Inequality but Largely Preserve Its Socioeconomic Structure](https://mlsanity.com/paper/2609.33055/) (2026)

## Abstract (arXiv)

Despite their global prevalence, many Large Language Models (LLMs) are aligned to a monolithic, often Western-centric set of values. This paper investigates the more challenging task of fine-grained value alignment: examining whether LLMs can emulate the distinct cultural values of demographic subgroups. Using Singapore as a case study and the World Values Survey (WVS), we examine the value landscape and show that even state-of-the-art models like GPT-4.1 achieve only 57.4% accuracy in predicting subgroup modal preferences. We construct a dataset of over 20,000 samples to train and evaluate a range of models. We demonstrate that simple fine-tuning on structured numerical preferences yields substantial gains, improving accuracy on unseen, out-of-distribution subgroups by an average of 17.4%. These gains partially transfer to open-ended generation. However, we find significant pre-existing performance biases, where models better emulate young, male, Chinese, and Christian personas. Furthermore, while fine-tuning improves average performance, it widens the disparity between subgroups when measured by distance-aware metrics. Our work offers insights into the limits and fairness implications of subgroup-level cultural alignment.