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Challenges and Strategies in Cross-Cultural NLP

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arxiv 2203.10020 v1 pith:7FSCN3K4 submitted 2022-03-18 cs.CL

Daniel Hershcovich , Stella Frank , Heather Lent , Miryam de Lhoneux , Mostafa Abdou , Stephanie Brandl , Emanuele Bugliarello , Laura Cabello Piqueras
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Ilias Chalkidis Ruixiang Cui Constanza Fierro Katerina Margatina Phillip Rust Anders S{o}gaard
This is my paper · ORCID
classification cs.CL
keywords languagecross-culturalculturedifferenceseffortsimportantservespeakers
verification ladder T0 review T1 audit T2 compute T3 formal
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Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GhazalBench: Evaluating LLM Understanding and Canonical Surface-Form Access in Persian Ghazals

    cs.CL 2026-02 conditional novelty 6.0 of 10

    LLMs can paraphrase Hafez's ghazals but mostly cannot complete their exact verses, while multiple-choice recognition largely closes the gap.

  2. XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad

    cs.CL 2026-01 conditional novelty 6.0 of 10

    XCR-Bench provides 4,100+ parallel sentences with 1,098 culture-specific items mapped to Hall's Triad, and shows LLMs struggle most with deeper, semi-visible cultural norms.

  3. Evaluation of Cultural Competence of Vision-Language Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    The paper proposes five theory-informed frameworks from visual cultural studies for evaluating cultural competence in vision-language models.

  4. "GenAI Defaults to Bias!" Gamify AI Literacy Through Reflections on Prompts

    cs.HC 2025-09 conditional novelty 5.0 of 10

    Playing ImaginAItion, a prompt-minimization party game, helped 30 adults recognize GenAI default biases and adjust their prompting strategies, according to pre-post survey coding.

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