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How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline

Paper · 2026 · Mir Tafseer Nayeem and Davood Rafiei

Summary

Popular chat platforms usually offer "English (US)" as their main English setting. The authors ask where in LLM development this preference comes from. They use British English as a fixed reference and build a resource of 1,813 matched AmE–BrE pairs covering spelling and word-choice contrasts, drawn from published references and online lexicons. They also propose DiAlign, a scorer that needs no training. It splits text into short overlapping n-grams, looks each one up in the American and British Google Books Ngram corpora, and turns weighted log-ratios into a probability for each variety. With these tools they audit six pretraining corpora and 21 post-training datasets. They then compare how compactly tokenizers encode each variant and how costly each variant is for models to predict. Finally, they classify model outputs under neutral prompts and under prompts asking for British English. Every stage leans American: data, tokenization, likelihood and default generation. Asking for British English moves outputs only partway. The study shows where targeted fixes could go and provides a reusable recipe for auditing variety bias.

Key points

  • Resource: 1,813 hand-compiled AmE–BrE variant pairs covering orthographic and lexical contrasts, to be released under CC BY-NC-SA 4.0 for non-commercial research.
  • DiAlign works as follows:
    • It takes all 2–5-gram spans and drops those containing named entities or made up only of stopwords.
    • It scores each remaining span by the log2 ratio of its American vs. British Google Books frequencies.
    • It down-weights spans with little difference between the two frequencies and boosts spans containing a lexicon entry.
  • DiAlign validation: on 1,500 balanced news passages (750 from HuffPost U.S. News, 750 from BBC England), it reached 93.18% accuracy and 93.38 F1. Errors cluster near the decision boundary. Narrowing the reference years to 2000–2022 barely changed results (92.91% accuracy).
  • In every one of the six pretraining corpora and every one of the 21 post-training sets, American forms are the majority. Under an equal-token-count control, British counterparts get higher per-token prediction cost in all ten checkpoints tested.
  • In generation, AmE is the default under neutral prompts and stronger in the formal NQ setting than in conversational ELI5. British prompting helps most in ELI5; for example, Llama-3.3 drops to roughly 30% AmE-classified outputs.

Interesting ideas

  • I1. Scoring a text by comparing each local span against two separate variety-specific frequency tables (signed log-ratios, not raw counts in one corpus) gives a cheap, explainable dialect signal that works on short inputs.
  • I2. Before measuring style, remove spans that contain named entities or only stopwords, so topic and filler words cannot pass as stylistic evidence; any style or register classifier can reuse this cleanup.
  • I3. Building matched counterfactual pairs that differ only in one regional variant, then comparing systems on both halves, isolates a single source of bias from everything else.
  • I4. When comparing model likelihoods of two paraphrases, keep only pairs with equal token counts so that length does not confound the comparison.
  • I5. Following one asymmetry across data, representation and output, instead of only testing outputs, shows where the bias starts and which component to fix.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (tokens): the en crate could tag spelling variants such as colour/color with a variety label and map them to one shared lemma, keeping a span-level trace of why a sentence was judged AmE or BrE.
  • I2 (learn): the same filter could be applied when checking whether silver data or LLM-teacher outputs drift toward one register, so the drift metric reflects form rather than subject matter.
  • I3 (tree): swap AmE/BrE variant pairs into treebank test sentences and compare perceptron parser and gparse LAS on the two versions, to check whether the parser quietly favours American text.
  • I4 (learn): when ranking alternative analyses or paraphrases with an LLM teacher, use token-matched pairs as a control alongside the existing negative controls.
  • I5 (learn): run the same stage-by-stage audit across training treebanks, tokenizer, parser and answer readers, to tell whether an error pattern comes from data or from model design.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of AlbertaEdmonton · Canada44 works · 3,236 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksEnglish · 2,263 works

Cite

Cite as Mir Tafseer Nayeem and Davood Rafiei. 2026. How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline. arXiv:2604.04204.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2604.04204 · arXiv 2604.04204

@misc{nayeem2026how,
  title = {{How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline}},
  author = {Mir Tafseer Nayeem and Davood Rafiei},
  year = {2026},
  doi = {10.48550/arxiv.2604.04204},
  eprint = {2604.04204},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2604.04204}
}

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