Current LLMs reproduce linguistic hierarchies by being built on dominant standard English varieties, producing a monolithic English that ignores regional variation¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims¶

q1 i? LLMs are built on dominant standard English varieties (often mainstream American English) and produce a monolithic version of English that ignores variation across other regional varieties. → Ugwuanyi 2025

Evidence¶

Ugwuanyi 2025¶

Ugwuanyi, K., Mair, C., Dovchin, S., Erdocia, I., Kuteeva, M., & Airemionkhale, E. (2025). Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes. https://arxiv.org/abs/2607.28505 doi:10.48550/arxiv.2607.28505

q1 · i? · theoretical · r2

Expert contribution to the structured scholarly dialogue: a sociolinguist working on linguistic diversity and AI states, from professional experience and cited literature, that LLMs "reproduce linguistic hierarchies" and produce monolithic English. No empirical test is reported in the article itself.

"As currently developed, LLMs reproduce linguistic hierarchies: they are built on dominant standard English varieties, often mainstream American English, and produce a monolithic version of English that ignores variation across other regional varieties of English."

Discussion¶

Related Claims¶

• Prompting experiments show LLMs perform badly at generating non-US national standard varieties and conflate Nigerian English with Nigerian Pidgin — a narrower finding that bears on this claim
• Economic, educational, and geopolitical inequalities structure differential access to AI capabilities, reproducing an epistemic hierarchy — related
• Automated grading systems may produce systematically inequitable outcomes for international and non-Western students — a narrower finding that bears on this claim
• Unstructured LLM use contributes to epistemic narrowing by privileging convergent outputs and dominant viewpoints — related
• Native speakers rated outputs of LLMs fine-tuned on Nigerian Pidgin English as understandable and context-aware, though no non-fine-tuned baseline was evaluated — related