| Challenge: | Cultural variation exists between nations, but also within regions . Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. |
| Approach: | They propose a method to measure cultural variation using a knowledge-guided lexical model using geolocated tweets. |
| Outcome: | The proposed method could help us better understand the way people communicate and build more culturally-aware NLP systems. |
Similar Papers
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
Copied to clipboard
Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
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. |
Towards Measuring and Modeling “Culture” in LLMs: A Survey (2024.emnlp-main)
Copied to clipboard
Muhammad Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Aji, Jacki O’Neill, Ashutosh Modi, Monojit Choudhury
| Challenge: | Existing models are biased towards Western, Anglocentric or American cultures, a problem that is arguably detrimental to the performance of LLMs. |
| Approach: | They analyze more than 90 recent papers that aim to study cultural representation and inclusion in large language models. |
| Outcome: | The proposed models are biased towards Western, Anglocentric or American cultures, despite their diversity and their robustness. |
On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) should be able to provide accurate information irrespective of the measurement system at hand . |
| Approach: | They use newly compiled datasets to test if this is true for seven open-source LLMs. |
| Outcome: | The proposed model can provide accurate information regardless of the measurement system at hand. |
Do LLMs model human linguistic variation? A case study in Hindi-English Verb code-mixing (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) do not reliably classify verb language preferences to match native speaker judgments. |
| Approach: | They investigate whether large language models (LLMs) model linguistic variation by comparing Hindi-English verb code-mixing with English verb karna. |
| Outcome: | The proposed models do not reliably classify verb language preferences to match native speaker judgments, but with specific supervision, some models do predict human preference to an extent. |
Cross-Lingual and Cross-Cultural Variation in Image Descriptions (2025.naacl-long)
Copied to clipboard
| Challenge: | Behavioural and cognitive studies report cultural effects on perception, but these are limited in scope and hard to replicate. |
| Approach: | They develop a method to accurately identify entities mentioned in captions and present in images, then measure how they vary across languages. |
| Outcome: | The proposed method corroborates previous studies showing that languages that are geographically or genetically closer mention entities more frequently than others. |
Culture is Not Trivia: Sociocultural Theory for Cultural NLP (2025.acl-long)
Copied to clipboard
| Challenge: | Cultural NLP has experienced rapid growth to meet the need to ensure language technologies are effective and safe across a pluralistic user base. |
| Approach: | They propose to use a well-developed theory of culture to clarify methodological constraints and affordances and offer theoretically-motivated paths forward to achieving cultural competence. |
| Outcome: | The proposed framework clarifies methodological constraints and affordances and offers theoretically-motivated paths forward to achieving cultural competence. |
Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspective (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a cognitive science research focus on aligning language spaces in their entirety . but, cognitive science has long focused on a local perspective . a new method for cross-lingual lexical alignment requires some methodology . |
| Approach: | They propose a method for analyzing kinship domain kinematics and a new method for contextualization . they propose kin-level validations and contextualizations to validate the results . |
| Outcome: | The proposed method analyzes synthetic validations and naturalistic validations using lexical gaps in the kinship domain. |
Culturally Aware Natural Language Inference (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Cultural norms are behavioral rules and conventions shared within specific groups, connecting cultural symbols and values. |
| Approach: | They propose a task that operationalizes cultural variations in language understanding through a natural language inference task that surfaces cultural variations as label disagreement between annotators from different cultural groups. |
| Outcome: | The proposed model can be evaluated at which levels it is culturally aware. |
Global Gallery: The Fine Art of Painting Culture Portraits through Multilingual Instruction Tuning (2024.naacl-long)
Copied to clipboard
| Challenge: | This study examines the ability of Large Language Models to encapsulate cultural nuances across diverse linguistic landscapes. |
| Approach: | They examine the efficacy of language-specific instruction tuning and the impact of pretraining on dominant language data in Large Language Models. |
| Outcome: | The findings highlight a nuanced landscape, with inconsistencies and biases, particularly in non-Western cultures. |