| Challenge: | Using CAVA, researchers can analyze country-specific biases encoded in large language models. |
| Approach: | They propose a visualization tool that allows users to identify biases in language models by adding country-based questions and models. |
| Outcome: | The proposed tool can be used to analyze the cultural competencies of large language models across the dimension of geographic locales. |
Similar Papers
Benchmarking Multi-National Value Alignment for Large Language Models (2025.findings-acl)
Copied to clipboard
Chengyi Ju, Weijie Shi, Chengzhong Liu, Jiaming Ji, Jipeng Zhang, Ruiyuan Zhang, Jiajie Xu, Yaodong Yang, Sirui Han, Yike Guo
| Challenge: | Existing studies on large language models focus on ethical reviews, failing to capture the diversity of national values. |
| Approach: | They propose a national value extraction pipeline to efficiently construct value assessment datasets and a model-based model with instruction tagging to process raw data sources. |
| Outcome: | The proposed benchmark evaluates the alignment of LLMs with the values of five major nations: China, the United States, the UK, France, and Germany. |
Global Voices, Local Biases: Socio-Cultural Prejudices across Languages (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on human biases are heavily skewed towards Western and European languages . despite growing interest in language models, there are several shortcomings in the literature . |
| Approach: | They scale the Word Embedding Association Test to 24 languages and add culturally relevant information for each language. |
| Outcome: | The proposed language models can reflect and often amplify the effects of bias across linguistic, cultural, and societal borders. |
Aligning Language Models to User Opinions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Personality is a defining feature of human beings, shaped by a complex interplay of demographic characteristics, moral principles, and social experiences. |
| Approach: | They use public opinion surveys to model past user opinions in addition to user demographics and ideology to achieve up to 7 points accuracy gains in predicting public opinions from survey questions. |
| Outcome: | The proposed model achieves 7 points accuracy gains in predicting public opinions from public opinion surveys across a broad set of topics. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
Copied to clipboard
Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |
StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)
Copied to clipboard
| Challenge: | Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability . |
| Approach: | They propose to measure stereotypical biases in four domains: gender, profession, race, and religion . they compare stereotypical and language modeling ability of popular models like BERT, GPT-2, RoBERTa and XLnet . |
| Outcome: | The proposed model shows strong stereotypical biases in gender, profession, race, and religion domains. |
Cognitive Effects and Biases in Large Language Models (2026.eacl-tutorials)
Copied to clipboard
| Challenge: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Approach: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Outcome: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent work in NLP has examined large language models for their understanding of cultural norms across countries, ignoring group consensus or possible multicultural environments. |
| Approach: | They apply cultural consensus theory to the World Values Survey to model multidimensional nuance by ignoring group consensus or over-regularizing consensus. |
| Outcome: | The proposed model misrepresents cultural structures by failing to form cohesive consensus or severely over-regularizing consensus. |
A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs (2025.acl-srw)
Copied to clipboard
| Challenge: | Large language models exhibit cultural and geopolitical biases when their outputs shape public opinion or reinforce dominant narratives. |
| Approach: | They define two types of bias in large language models: model bias and inference bias through a two-phase evaluation. |
| Outcome: | The proposed framework evaluates large language models on factual and disputable questions across four languages and question types. |
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages (2023.findings-acl)
Copied to clipboard
| Challenge: | Sentiment analysis systems are used in hundreds of products and languages . Gender and racial biases are well-studied in English, but understudied elsewhere . |
| Approach: | They build a counterfactual evaluation corpus for gender and racial/migrant bias in four languages. |
| Outcome: | The evaluation corpus reveals which models have less bias and pinpoints changes in model bias behaviour, enabling more targeted mitigation strategies. |
Incorporating Diverse Perspectives in Cultural Alignment: Survey of Evaluation Benchmarks Through A Three-Dimensional Framework (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) serve diverse global audiences, making it critical for responsible AI deployment across cultures. |
| Approach: | They propose a framework that conceptualizes alignment along three dimensions: Cultural Group, Cultural Elements and Awareness Scope. |
| Outcome: | The proposed framework reveals critical gaps between benchmarks and real-world cultural biases . region dominates cultural group representation, social and political relations dominates coverage . majority of datasets adopt majority-focused Awareness Scope approaches . |