# From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test

arXiv: https://arxiv.org/abs/2505.18562 · HTML: https://mlsanity.com/paper/2505.18562/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 27 of 27)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Refusal in Language Models Is Mediated by a Single Direction](https://mlsanity.com/paper/2406.11717/) (2024)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://mlsanity.com/paper/2406.09948/) (2024)
- [Investigating Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2402.13231/) (2024)
- [Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization](https://mlsanity.com/paper/2406.00045/) (2024)
- [Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions](https://mlsanity.com/paper/2309.12342/) (2023)
- [CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies](https://mlsanity.com/paper/2404.15238/) (2024)
- [Word Embeddings Are Steers for Language Models](https://mlsanity.com/paper/2305.12798/) (2023)
- [Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs](https://mlsanity.com/paper/2310.07251/) (2023)
- [Democratizing Neural Machine Translation with OPUS-MT](https://mlsanity.com/paper/2212.01936/) (2022)
- [Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations](https://mlsanity.com/paper/2502.07068/) (2025)
- [Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense](https://mlsanity.com/paper/2405.04655/) (2024)
- [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025)
- [Assessing LLMs for Moral Value Pluralism](https://mlsanity.com/paper/2312.10075/) (2023)
- [Can Language Models Reason about Individualistic Human Values and Preferences?](https://mlsanity.com/paper/2410.03868/) (2024)
- [Self-Pluralising Culture Alignment for Large Language Models](https://mlsanity.com/paper/2410.12971/) (2024)
- [Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge](https://mlsanity.com/paper/2404.06833/) (2024)
- [Self-Alignment: Improving Alignment of Cultural Values in LLMs via In-Context Learning](https://mlsanity.com/paper/2408.16482/) (2024)
- [MuLan: A Study of Fact Mutability in Language Models](https://mlsanity.com/paper/2404.03036/) (2024)
- [Does Cross-Cultural Alignment Change the Commonsense Morality of Language Models?](https://mlsanity.com/paper/2406.16316/) (2024)
- [Effectively Steer LLM To Follow Preference via Building Confident Directions](https://mlsanity.com/paper/2503.02989/) (2025)
- [CAReDiO: Cultural Alignment via Representativeness and Distinctiveness Guided Data Optimization](https://mlsanity.com/paper/2504.08820/) (2025)
- [What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs](https://mlsanity.com/paper/2501.03569/) (2025)

## Built on by (most cited first, 2 of 2)

- [EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis](https://mlsanity.com/paper/2601.22873/) (2026)
- [ALIGN: Word Association Learning for Cultural Alignment in Large Language Models](https://mlsanity.com/paper/2508.13426/) (2025)

## Abstract (arXiv)

The human-centered word association test (WAT) serves as a cognitive proxy, revealing sociocultural variations through culturally shared semantic expectations and implicit linguistic patterns shaped by lived experiences. We extend this test into an LLM-adaptive, free-relation task to assess the alignment of large language models (LLMs) with cross-cultural cognition. To address culture preference, we propose CultureSteer, an innovative approach that moves beyond superficial cultural prompting by embedding cultural-specific semantic associations directly within the model's internal representation space. Experiments show that current LLMs exhibit significant bias toward Western (notably American) schemas at the word association level. In contrast, our model substantially improves cross-cultural alignment, capturing diverse semantic associations. Further validation on culture-sensitive downstream tasks confirms its efficacy in fostering cognitive alignment across cultures. This work contributes a novel methodological paradigm for enhancing cultural awareness in LLMs, advancing the development of more inclusive language technologies.