2026-06-01原文2 min readUpdated: 2026-06-01

When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models

This study investigates how LLMs exhibit cultural bias by favoring global narratives over local contexts, introducing the concept of 'global narrative dominance'. Using the CulturalNB dataset for Bangla, they find that English questions increase global substitution and institutional framing, reducing local perspective coverage. Local evidence improves consistency but does not eliminate epistemic shifts.

SourcearXiv Computational LinguisticsAuthor: Md Arid Hasan, Ruwad Naswan, Farhan Samir, Sharifa Sultana, Syed Ishtiaque Ahmed

[2605.30481] When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models

[Submitted on 28 May 2026]

Title:When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models

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Abstract:Large language models (LLMs) are widely used as cross-lingual knowledge interfaces. However, culturally grounded questions often reflect globally dominant narratives rather than local contexts. We study this failure mode as \textit{global narrative dominance} in Bangla, a low-resource cultural context. We introduce \texttt{CulturalNB}, a dataset of 717 manually curated Bengali cultural instances with parallel Bangla--English question--answer pairs and supporting evidence, metadata, and sociocultural annotations. Using question-only and evidence-based prompting, we evaluate nine state-of-the-art LLMs with human and two independent LLM judges across metrics for cross-lingual consistency, language anchoring, global substitution, institutional bias, and epistemic perspective coverage. Results show that questions asked in English systematically increase global substitution and institutional framing while reducing local perspective coverage. Local evidence improves factual consistency and perspective coverage, but does not eliminate language-induced epistemic shifts. These findings suggest that cultural failures in LLMs are not only missing-knowledge errors but also failures of grounding and narrative prioritization.

Comments: Submitted to ARR

Subjects:

Computation and Language (cs.CL)

MSC classes: 68T50

ACM classes: F.2.2; I.2.7

Cite as: arXiv:2605.30481 [cs.CL]

(or arXiv:2605.30481v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.30481

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Md Arid Hasan [view email] [v1] Thu, 28 May 2026 18:58:32 UTC (3,998 KB)

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