mlsanityData to 14 Oct 2026

Building Knowledge-Guided Lexica to Model Cultural Variation

arXiv 2406.11622 ↗First posted 17 Jun 2024cs.CLcited by 11

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Abstract (arXiv)

Cultural variation exists between nations (e.g., the United States vs. China), but also within regions (e.g., California vs. Texas, Los Angeles vs. San Francisco). Measuring this regional cultural variation can illuminate how and why people think and behave differently. Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. In this work, we introduce a new research problem for the NLP community: How do we measure variation in cultural constructs across regions using language? We then provide a scalable solution: building knowledge-guided lexica to model cultural variation, encouraging future work at the intersection of NLP and cultural understanding. We also highlight modern LLMs' failure to measure cultural variation or generate culturally varied language.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 13 of its 54 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

13 of 54 references.

References · 13

  1. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  2. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
  3. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 234
  4. 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 149
  5. 2023Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensionscited by 112
  6. 2023Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Dutiescited by 97
  7. 2023Multilingual Language Models are not Multicultural: A Case Study in Emotioncited by 54
  8. 2019MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Predictioncited by 31
  9. 2021Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexicacited by 23
  10. 2016Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corporacited by 19
  11. 2020Learning and Evaluating Emotion Lexicons for 91 Languagescited by 13
  12. 2018The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictionscited by 10
1 more
  1. 2023The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?cited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 4.

11 papers.

  1. 2024The FIX Benchmark: Extracting Features Interpretable to eXpertscited by 5
  2. 2025Culturally-Aware Conversations: A Framework & Benchmark for LLMscited by 5
  3. 2025Adaptively profiling models with task elicitationcited by 4
  4. 2025Addressing Longstanding Challenges in Cognitive Science with Language Modelscited by 3
  5. 2024Inducing lexicons of in-group language with socio-temporal contextcited by 2
  6. 2025T-FIX: Text-Based Explanations with Features Interpretable to eXpertscited by 2
  7. 2025Splits! Flexible Sociocultural Linguistic Investigation at Scalecited by 0
  8. 2026Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communitiescited by 0
  9. 2026Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Modelscited by 0
  10. 2026CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systemscited by 0
  11. 2026FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditingcited by 0