arXiv:2607. 02369v1 Announce Type: cross Abstract: LLMs stage a new form of cultural encounter that is massive, automated, and monolingual.
arXiv:2606. 28333v1 Announce Type: cross Abstract: \begin{quote} The biases in Large Language Models' (LLMs) outputs remain inadequately theorised, particularly from the perspective of the Global South.
arXiv:2607. 11808v1 Announce Type: cross Abstract: This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography.
The paper proposes a new way to evaluate language models on cultural understanding by focusing on interpretive depth rather than just factual recall. It argues that literary interpretation, where scholars can disagree yet still assess the quality of evidence, provides a useful framework for testing how models handle cultural references, reuse, and transformation across texts. The authors suggest linking evidence-centered benchmarks, preserving scholarly disagreement, and conducting model-development experiments on literary data, with Danish literature as a starting point for broader applications.
arXiv:2607. 06544v1 Announce Type: new Abstract: As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization.
arXiv:2607. 01776v1 Announce Type: cross Abstract: In the age of AI, what will be good knowledge?
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.
arXiv:2606. 01929v1 Announce Type: new Abstract: Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public.
arXiv:2606.26040v2 Announce Type: replace Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...
The paper investigates how Large Language Models (LLMs) construct fictional worlds, specifically examining setting as a measurable aspect of storyworld creation. By generating 1,000 AI stories per model in English and German and comparing them to human-authored fiction from Project Gutenberg, the authors classify narrative space into five categories—action, perceived, visual, descriptive, and no space—using fine‑tuned BERT classifiers. Results show that human texts mainly use action space, grounding narratives in character-environment interaction, while LLMs consistently overproduce perceived space, focusing on atmosphere and affect, with this pattern varying by model and language.
arXiv:2606. 22748v2 Announce Type: replace-cross Abstract: Some professional authors are beginning to use AI tools to help produce their fiction writing.
arXiv:2601. 15828v4 Announce Type: replace-cross Abstract: This study investigates whether professional translators without prior specialized training can reliably identify short stories generated in Italian by artificial intelligence (AI).
The paper investigates how large language models (LLMs) represent national cultural change over time, using more than two decades of World Values Survey data and the Inglehart‑Welzel cultural map. It finds that while LLMs generally place countries near their most recent surveyed positions, their representations lag behind current data, under‑capture the magnitude of change, introduce spurious movements, and rarely reproduce trajectory reversals. These temporal inaccuracies reveal a flattening effect that limits the models’ cultural awareness and raises concerns for evaluation, representational harms, and governance of culturally aware AI systems.