The paper explores how "culture" can be operationalised in Natural Language Processing (NLP) and what this reveals about the possibilities and limits of considering a plurality of cultural backgrounds in technological design. It proposes that cultural alignment cannot be achieved only by adding more examples of "other cultures", rather it requires plural epistemologies: allowing multiple, locally grounded ways of knowing.
The chapter discusses how large language models (LLMs) simplify the transformation of cultural material into analyzable variables and how LLM-driven agentic systems broaden machine assistance across research workflows. It examines key aspects of this emerging paradigm and its broader implications, arguing that LLMs increase scale, methodological diversity, and delegation while largely preserving the fundamental logic that underpins cultural measurement and claim formation.
By Andres Karjus
QQ is a language metadata toolkit designed for multilingual NLP research. It aggregates diverse language metadata into a graph of varieties, scripts, regions, identifiers, names, and relations, and offers access via a Python API, CLI, and browser explorer. The toolkit enables normalization of identifiers, metadata retrieval, relation traversal, and discovery of external resources containing a language, and is demonstrated through audits of the HuggingFace Hub, linking resources with different identifier systems, and generating reproducible language-reporting tables.
By Wessel Poelman, Yiyi Chen, Miryam de Lhoneux
arXiv:2405.06818v2 Announce Type: replace
Abstract: Natural Language Processing (NLP) for Ghana's 73 living indigenous languages remains deeply fragmented, under-resourced, and heavily skewed toward...
By Sheriff Issaka, Erick Rosas Gonzalez, Colene Agbo, Evans Kofi Agyei, Shruti Tyagi, John Emeka Eze, Enock Appiah Tieku, Junlin Fang, Thanh Do Nguyen, Juliet Arthur, Zhaoyi Zhang, Mihir Heda, Keyi Wang, Yinka Ajibola, Rebecca Akpanglo-Nartey, Frank Lawrence Nii Adoquaye Acquaye, Dennis Owusu, Jerry John Kponyo, Stephen Moore, Isaac Wiafe, Sean Du
KULTURE Bench is a new evaluation framework created to assess language models within the Korean cultural context. It includes datasets of cultural news, idioms, and poetry and tests models at word, sentence, and paragraph levels for cultural comprehension and reasoning. Experiments with models trained on different corpora show that current models still lack deep understanding of Korean cultural texts.
By Xiaonan Wang, Jinyoung Yeo, Joon-Ho Lim, Hansaem Kim
arXiv:2608.30107v1 Announce Type: cross
Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and i...
By Joan Nwatu, Tsedeniya Solomon Amare, Longju Bai, Bontu Fufa Balcha, Zayd Bashir, Angana Borah, Zara Burzo, Yubin Choi, Naihao Deng, Samika Gupta, Michel Faloughi, Claude Kwizera, Ziqiao Ma, Cynthia Yacel Fuertes Panizo, Ellie Seehorn, Hui Shen, Jiayi Tang, Zesen Zhao, Boyuan Zheng, Rada Mihalcea
The paper "Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems" investigates how commercial text‑to‑image models silently modify user prompts before generating images, a step that is often hidden from users. Using the multilingual benchmark WORLDVIEW, the authors audit the revision layer in DALL‑E‑3, Imagen‑4, and GPT‑Image‑1.5, finding that non‑Western and non‑Anglophone contexts are disproportionately marked, reduced to narrow vocabularies, and stereotyped. The study demonstrates that the revision layer itself is a previously undocumented causal source of cultural stereotyping, underscoring the need to audit deployed systems rather than just the underlying models.
By Aleksandra Urman, Elsa Lichtenegger, Salima Jaoua, Azza Bouleimen, Robin Forsberg, Corinna Hertweck, Stefania Ionescu, Nicol\`o Pagan, Ancsa Hannak, Joachim Baumann
Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy. However, geographic metadata is...
arXiv:2608. 11002v1 Announce Type: cross Abstract: Text-to-image (T2I) generation has achieved remarkable progress in recent years.
By Sicheng Zhang, Zhonghao Yan, Binzhu Xie, Shi Qiu, Muzammal Naseer, Naveed Akhtar, Mubarak Shah
arXiv:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
By Yiming Wang, Jiayuan Di
WikiOriginQA is a newly introduced QA dataset that is automatically constructed by using the creation dates of different Wikipedia language editions to represent the origins of knowledge. The dataset allows for the analysis of cultural bias in large language models, with experiments showing a bias toward Anglophone cultures and highlighting the importance of knowledge propagation into the Anglosphere. This automated approach offers scalable cultural bias evaluation across diverse languages without extensive manual annotation.
By Kento Haneda, Yosuke Kishinami, Ryo Fujii, Keisuke Sakaguchi, Makoto Morishita
The paper explores how large language models (LLMs) can adapt culturally through repeated social interactions in a sandbox setting. It reframes cultural adaptation as a continuous norm‑assimilation process, showing that memory mechanisms enable longitudinal learning, transfer across different social contexts, and multilingual norm assimilation in five languages. These results establish a foundation for interaction‑driven cultural adaptation in LLMs.
By Chen Cecilia Liu, Roi Reichart, Anna Korhonen