mlsanityData to 14 Oct 2026

Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon

arXiv 2506.01675 ↗First posted 2 Jun 2025cs.CLcited by 5

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Abstract (arXiv)

Despite substantial research efforts evaluating how well large language models~(LLMs) handle global cultural diversity, the mechanisms behind their cultural knowledge acquisition, particularly in multilingual settings, remain unclear. We study this question by investigating how cultural knowledge transfers across languages during language adaptation of LLMs. We introduce an interpretable framework for studying this transfer, ensuring training data transparency and controlling transfer effects. Through a study of four non-Anglophonic cultures, we observe bidirectional cultural transfer between English and other high-resource languages, while low-resource languages primarily transfer knowledge to English with limited reverse flow. To explain this asymmetric phenomenon, we propose a frequency-based hypothesis: cultural knowledge appearing more frequently in the pretraining data transfers more easily, which is supported by empirical analysis of the training corpora.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

21 of 33 references.

References · 21

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  3. 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
  4. 2021Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Modelscited by 132
  5. 2024Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontiercited by 129
  6. 2023Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Modelscited by 118
  7. 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
  8. 2022Language Contamination Helps Explain the Cross-lingual Capabilities of English Pretrained Modelscited by 92
  9. 2024How Do Large Language Models Acquire Factual Knowledge During Pretraining?cited by 90
  10. 2024Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Datacited by 85
  11. 2023PolyLM: An Open Source Polyglot Large Language Modelcited by 64
  12. 2024Massively Multi-Cultural Knowledge Acquisition & LM Benchmarkingcited by 63
9 more
  1. 2024CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Promptingcited by 55
  2. 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Reviewcited by 50
  3. 2023Language Versatilists vs. Specialists: An Empirical Revisiting on Multilingual Transfer Abilitycited by 27
  4. 2024BertaQA: How Much Do Language Models Know About Local Culture?cited by 23
  5. 2022Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translationcited by 21
  6. 2024Large Language Models Are Cross-Lingual Knowledge-Free Reasonerscited by 16
  7. 2024Tracing the Roots of Facts in Multilingual Language Models: Independent, Shared, and Transferred Knowledgecited by 14
  8. 2020A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERTcited by 8
  9. 2024Attributing Culture-Conditioned Generations to Pretraining Corporacited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.