Does using a non-English language affect an LLM's answer quality?

Quality is more than grammatical fluency

An LLM can write polished sentences in a language and still give poor answers in it. A useful evaluation separates at least six dimensions:

• Task correctness: Does it solve the question, extract the right field, write working code, or make the correct decision?
• Factual grounding: Are claims supported by trustworthy and relevant sources, including local sources where the question requires them?
• Instruction following: Does it obey the requested language, format, reading level, and scope?
• Linguistic quality: Is the output comprehensible, idiomatic, and appropriate for the requested variety rather than merely grammatical?
• Cultural and contextual fit: Does it understand locally meaningful institutions, names, units, practices, and implied context?
• Safety and fairness: Does it refuse or redirect harmful requests consistently, without treating a dialect or community as suspicious or invisible?

The same model can score well in one of these dimensions and badly in another. A translation task might preserve the main proposition while producing an inappropriate honorific. A health answer can be fluent yet cite guidelines from the wrong country. A support bot might understand standard Spanish but fail on regional vocabulary or a bilingual sentence. Calling all of these failures "worse English" hides the operational problem.

Why quality can differ by language

Training-data imbalance

Large language models learn statistical patterns from their training and fine-tuning data. Languages with abundant, varied, high-quality text, code, reference material, and human feedback usually give the model more opportunity to learn forms and facts. Low-resource languages may have less digitised material, more narrow domains, noisier web text, fewer annotated instructions, and less evaluation data. Dialects can be even less represented because web sources standardise spelling or label dialect as informal noise.

Multilingual training can help through transfer. Shared scripts, vocabulary, syntactic patterns, and parallel concepts allow a model to learn from related languages. But capacity and mixture choices create tradeoffs. In an experiment spanning 250 languages, modest additional multilingual data helped low-resource language modelling in some conditions, while sufficiently large added multilingual data hurt both low- and high-resource languages, likely because finite model capacity must represent competing distributions. The benefit also depended on the syntactic similarity of added languages. When Is Multilinguality a Curse?

This is why language support is not ordered simply by number of speakers or whether a language is European. A language can have many speakers but relatively little digitised, permissively usable, domain-relevant data. Another can be well served by a dedicated model, careful instruction tuning, and local evaluation. A model card's supported-language list is useful evidence, but it is not a guarantee of equal quality across tasks or dialects.

Tokenization and effective context

Before an LLM sees text, a tokenizer converts it into tokens. A token is not necessarily a word or character. Common subword and byte-level tokenizers may use a small number of tokens for a frequent English phrase but many tokens for the same idea in another script or a word with rich morphology. More tokens can use more of a fixed context window, increase latency or cost where usage is token-priced, and give the model more opportunities to make a generation error.

Tokenization is not the entire explanation for multilingual performance. The vocabulary learns from the training mix, and better training data or a better model can offset an inefficient split. Still, it is measurable and relevant. The Token Tax study evaluated ten LLMs on AfriMMLU in 16 African languages and found token fertility consistently predicted lower accuracy in its setting. The Token Tax Do not use this result to rank an individual user's language. Use it to check the actual tokenizer and end-to-end task results for the languages your system serves.

Dialects and code-switching

A language label such as Arabic, English, Hindi, or Spanish is often too coarse. Spelling, grammar, vocabulary, politeness, and cultural references vary across regions and communities. Standard-language benchmarks may not expose a model that paraphrases a dialect away, mistakes a regional term for a typo, or responds in a mismatched variety. A 2025 study of conversational understanding found degraded performance on Indian English relative to US English for all evaluated models. Evaluating Dialect Robustness of Language Models via Conversation Understanding

Code-switching is the normal practice of alternating languages in one utterance or conversation. It is not necessarily a malformed prompt. A user might write a Hindi-English technical question, an Arabic sentence with French legal terms, or a Spanish message containing English product labels. Research evaluating multilingual LLMs across four code-switching tasks found the models were not yet reliable code-switchers, with behavior varying by task and language pair. Multilingual Large Language Models Are Not (Yet) Code-Switchers

For a product, preserve the user’s original wording and test realistic mixing patterns. Aggressive normalisation, forced language detection, or automatic translation can delete meaning in a name, quote, code fragment, or regional term. Ask a concise clarification when the intended language or term is genuinely ambiguous, rather than silently choosing an interpretation.

Prompt language and answer language are separate choices

The language used to ask a question and the language requested for the answer can be the same or different. Neither arrangement wins reliably across all tasks.

Research has found genuine prompt-language effects, but not a rule that English always wins. In a cross-lingual grammaticality study, prompting in another language often improved performance on questions about English despite English being dominant in the models' training data. To Ask LLMs about English Grammaticality, Prompt Them in a Different Language Conversely, a multilingual medical multiple-choice study reported that English prompts generally produced better accuracy for its tested tasks and models, while leading models could be comparable with language-matched German and French prompts. Multilingual Medical Multiple-Choice Evaluation

Those findings are not contradictory. They test different models, languages, tasks, and measurements. They support an experimental decision rule: for a consequential workflow, compare the small number of prompt and answer arrangements that are plausible for your users, then keep the arrangement that wins on native-language acceptance criteria. Do not make users translate sensitive or nuanced questions into English simply because an old benchmark was English-only.

What benchmarks can and cannot tell you

Multilingual benchmark scores are evidence, not a universal language-quality certificate. Common limitations include:

• Translationese: A benchmark translated from English can retain English phrasing, assumptions, or answer patterns. It may measure how well the model handles translated text rather than natural writing in the target language.
• Locality loss: A translated question can omit a locally ordinary concept, legal framework, school curriculum, or cultural reference. A correct answer in English may not answer the native question people actually ask.
• Uneven task coverage: Many benchmarks favour multiple choice, translation, or formal reasoning and underrepresent open-ended support, public services, informal writing, dialects, and safety-sensitive use.
• Contamination: Popular benchmark items or close variants may have appeared in model training data, inflating a score without showing generalisation.
• Automated judging limits: An LLM judge or reference-overlap metric can reward a literal translation and miss a culturally inappropriate, misleading, or unsafe response.

The PARIKSHA research group identifies insufficient linguistic diversity, contamination, and lost local cultural nuance in translated benchmarks as multilingual evaluation problems, and uses native-speaker-developed prompts in its study of human and LLM evaluators. PARIKSHA A 2026 study of translated benchmark validity likewise asks whether translated scores add information about native performance after accounting for model size and language proficiency. Gold vs. Translation

Use an English benchmark to learn something about an English benchmark. Use a well-designed multilingual benchmark to compare similar conditions. Use neither as a substitute for testing native-written material from the service, market, or community in which the system will operate.

A practical evaluation plan

Start with the promised experience, not the model's language list. State the task, target locales and dialects, acceptable answer languages, critical terminology, safety issues, and error costs. Decide whether names, quotations, documents, code, and retrieved sources should remain in their original languages. This prevents a vague promise such as "supports French" from concealing that the actual use is Quebec consumer support, West African French-language health information, or a French-English engineering team.

Build a native evaluation set for each priority audience. Include naturally written prompts, not only translations; standard and everyday registers; local entities and date, number, and unit conventions; domain vocabulary; ambiguous cases; and ordinary code-switching. Sample examples from the intended workflow with consent and privacy safeguards, then remove personal data. Keep a held-out slice so that prompts and retrieval content are not tuned repeatedly against the same cases.

Have multiple qualified native speakers rate blinded outputs. They should assess task correctness, fluency, dialect appropriateness, cultural fit, citations and factual accuracy, safety, and whether the answer is understandable to the intended reader. Recruit reviewers who actually use the target variety and relevant domain, rather than treating a speaker of a distant standard variety as interchangeable. Reconcile disagreements with a documented rubric and inspect the examples behind aggregate scores.

Compare the candidate models and prompt arrangements under identical conditions. For each native prompt, test at least a same-language answer and the bilingual arrangement that the product might use. Measure the requested quality dimensions separately. Track latency, token count, and cost, but do not let lower token count override a loss of meaning. For high-stakes health, legal, financial, public-service, or safety uses, require domain-expert review and maintain an escalation path. Language translation does not remove the underlying accuracy obligation.

Example

Hypothetical example: A city builds a benefits-information assistant for Spanish-speaking residents, including speakers who alternate Spanish and English when discussing forms. The initial English-only evaluation shows high question-answering accuracy, so the team considers automatically translating all resident messages to English and translating replies back to Spanish.

The team instead writes a native Spanish and Spanish-English evaluation set with local benefit names, incomplete questions, code-switched form fields, and requests from different communities. Native reviewers compare three arrangements: Spanish input and Spanish answer, Spanish input and English answer for an internal agent, and an automatic English translation pipeline with a Spanish answer. They separately score factual correctness against the city policy, preservation of form names, clarity, dialect fit, and whether safety or eligibility qualifiers survive translation.

The likely outcome need not be that one configuration wins every task. The public assistant may use Spanish end to end to preserve resident meaning, while an internal summary is generated in English with the original Spanish attached for review. Choose the workflow from measured results, make language limitations visible, and provide review for confusing or consequential cases.

Ways to improve quality while preserving meaning

• Choose models whose language support and safety documentation covers your priority languages, then verify those claims on your own tasks.
• Let users write in their preferred language or mixture. Make output language and desired locale explicit, for example, "Answer in Brazilian Portuguese" or "Use Moroccan Arabic terminology where appropriate."
• Retrieve source material in the language most authoritative for the question. Preserve quotations and named entities, and say when a source was translated or a local guideline was unavailable.
• Use high-quality translation as one candidate component, not a hidden guarantee. Evaluate translation loss, technical terminology, and safety qualifiers before putting it in a high-impact pipeline.
• Keep source language, prompt language, and answer language as separate logged fields, subject to privacy controls. This makes regressions observable without treating language as a proxy for identity.
• Test code-switching, regional vocabulary, spelling variation, and informal writing. Do not silently "correct" these inputs into a different meaning.
• Measure and monitor language-specific error, refusal, hallucination, escalation, and user-satisfaction rates. Inspect error examples, not only a cross-language average.
• Involve local language communities and domain experts in test creation, review, and remediation. Pay reviewers and respect data-governance requirements.

For an individual, the lightweight version is simple. Ask in the language that lets you express the question accurately. If the answer matters, try a second formulation in another language only as a comparison, state the requested output language, check claims against original sources, and ask a native-speaking expert when precision or local norms matter. A difference between two answers is a reason to investigate, not evidence that one language is inherently better.