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Yes, an AI can recommend different brands when you ask the same question in different languages. The language may act as a hidden signal for market and cultural context, activate different learned associations, or expose differences in the model’s multilingual capability. That does not prove a particular brand is preferred by speakers of one language. It means the recommendation is conditional on the wording, locale assumptions, data and model behavior.
You can measure the effect with a controlled experiment: keep the model, market, instructions, tools and sampling settings fixed; use meaning-matched prompts written or checked by fluent speakers; repeat every condition; and compare brand overlap, ranking, rationale and human judgments. Treat language and market as separate variables so an apparent language effect is not simply an unspoken location change.
What a language change can signal to an AI
A language is more than a code for the same sentence. In a language-only prompt, the model may infer a region, audience, shopping environment or cultural context that you never stated. Those inferences can alter which products seem relevant, which brands are familiar, and what constraints the answer assumes.
Implicit locale assumptions
Google Research’s LocQA study tested 2,156 questions in 12 languages across 32 models. Its publication page reports a global preference for answers relevant to the United States even when questions were asked in languages other than English. It also found a tendency to prefer larger-population locales when several locales shared a language. Those findings show that language can cue geographic assumptions; they do not directly show that a specific consumer brand wins in a specific language.
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For example, a request in a language used across several countries might lead a model to assume the largest market, a US-centric market, or no precise market at all. Each assumption changes the pool of products, prices, regulations and brands that appear sensible. If you name the country explicitly, you remove one source of ambiguity, but you should still test whether wording in the local language changes the answer.
Uneven information across languages
Models learn from data distributed unevenly across languages. Product descriptions, reviews, retailer pages, government rules and local brand histories may be plentiful in one language and sparse in another. This is a plausible explanation to investigate when recommendations diverge, not a proven cause for any individual brand shift in the studies cited here. A model can know a brand in both languages yet have richer evidence, more recent text or more market-specific terminology in one.
Different language capabilities
Translation quality, entity recognition, spelling variants and instruction following can vary by language. The same budget or product requirement may be interpreted less precisely in one version of a prompt. The ACL 2025 study of multilingual advice for university applications, travel and relocation reported significant differences in scores and reasoning structure for non-English settings. That supports the broader conclusion that decision-style outputs can vary by language, while leaving consumer brand rankings as a narrower question that requires its own test.
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The strongest evidence concerns locale questions and advice tasks rather than shopping lists. Keep that boundary visible when you interpret an experiment.
| Source and date | What was evaluated | Published figure or finding | How it applies to brand tests |
|---|---|---|---|
| Google Research, LocQA (publication year not shown on the opened page) | Locale questions in 12 languages and 32 models | 2,156 questions; a global tendency toward US-relevant answers and preferences for larger-population locales when languages span multiple locales | Direct evidence that language can cue locale assumptions; not a direct brand-preference measurement |
| ACL 2025 multilingual advice study | University applications, travel and relocation advice | Different scores and reasoning structures in non-English settings | Evidence that recommendation-like reasoning can change with language; consumer brands remain an open empirical question |
| Microsoft Research, 2023 multilingual evaluation work | Seven benchmarks covering reasoning, understanding and generation | More than 10 points of average improvement from cross-lingual transfer (XLT) on arithmetic reasoning and open-domain question answering | Shows that evaluation results depend on language and method; the improvement must not be generalized to brand recommendations |
| PARIKSHA repository summary, 2024 | Model evaluation in 10 Indic languages | 30 models, 90,000 human evaluations and 30,000 LLM-based evaluations | Demonstrates why evaluator language and method need validation in a recommendation study |
Microsoft’s MEGA project documentation describes three useful evaluation setups: monolingual, zero-shot cross-lingual and translated-test. It states: “We use prompt-based approach to evaluate LLMs on multilingual benchmark. For each task we divide our prompt into two portions: i) Few-shot Labelled examples and ii) Test Example to evaluate.” The setup is a vocabulary for designing comparisons, not evidence that one setup is best for brands.
The LocQA publication page summarizes its result this way: “Inter-lingually, we show a global bias towards answers relevant to the US-locale, even when models are asked in languages other than English.” Read that as a locale finding. It does not establish that US brands are universally preferred.
Separate language from market before you test
The central design problem is confounding. If you ask in French and leave the market unstated, the model may switch both language and assumed country. You cannot tell which variable changed the answer.
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Choose one market, such as Canada, and state it identically in every prompt. Ask for the same product category, user need, budget, availability requirement and time frame in each language. This estimates the effect of language while holding the intended market constant.
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Add an explicit-market arm
Run a second set in which language and market are deliberately paired: for example, English with Canada, French with Canada, English with France and French with France. The language-only and language-plus-market results reveal whether the model’s implicit locale assumption is doing most of the work.
Record the model’s inferred context
Ask the model to state the market, currency, availability assumptions and consumer constraints it used before giving recommendations. Do not treat that explanation as proof of its internal process; use it as observable output that can be coded. An answer that silently changes currency or retailer availability is evidence of a context shift that should be reported.
Build meaning-matched prompts
Literal translation is not a sufficient control. Microsoft Research identifies loss of local and cultural nuance in translated benchmarks as a challenge for multilingual evaluation. A literal rendering can sound unnatural, alter politeness, change a technical term or introduce a constraint that was absent from the original.
Write a master specification
Define the request in structured form before drafting any language version:
- Product category and use case.
- User profile and required features.
- Budget, currency and purchase horizon.
- Intended market and availability rules.
- Number of recommendations and ranking criteria.
- Whether the model may browse, retrieve documents or use a location signal.
Then have fluent speakers independently write or review each version. Ask reviewers to flag terms that have no exact equivalent, ambiguous units, culturally loaded wording and differences in formality.
Example prompt pair
A controlled prompt might ask for “three laptop brands available in the Canadian market for a university student, under CAD 1,200, prioritizing battery life and repairability. Explain the ranking and identify any uncertainty.” Produce a natural-language version in each test language, preserving every constraint. Do not translate the currency, country or budget into a different market unless that is the experimental variable.
Keep variants traceable
Assign an identifier to every prompt and store the original, language, reviewer, revision date and any unresolved ambiguity. If a reviewer changes a phrase after seeing model outputs, mark the change and rerun earlier conditions; otherwise you risk adjusting the test toward a preferred result.
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Language is only interpretable when other inputs are stable. For every run, record:
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- Model name, version or deployment identifier.
- System instructions and developer messages.
- Conversation history and memory state.
- Enabled tools, retrieval indexes and document snapshots.
- Location, currency, account settings and personalization signals.
- Sampling parameters such as temperature, top-p, seed and response limit.
- Run date and time, because prices, inventory and model behavior change.
If the production system searches the web, save the query, result list or retrieved passages. A language can change search terms and therefore the evidence shown to the model. If location services are enabled, either fix the location for all conditions or create a separate location experiment. Never mix a language change with a model upgrade, a new retrieval corpus or a changed system prompt.
Run the experiment repeatedly
One answer is not a measurement. Sampling, transient retrieval results and model nondeterminism can alter a ranking even when the prompt is identical. Run each language-and-market condition several times, using a fixed seed when the system supports one and multiple seeds when you want to estimate ordinary variation.
- Freeze the test plan. Specify conditions, run count, stopping rule and metrics before inspecting results.
- Randomize order. Interleave languages or randomize their run order so a temporary service change affects conditions evenly.
- Save complete records. Keep the exact prompt, settings, retrieved material, full response and any refusal or error.
- Separate retries from observations. Log timeouts and malformed outputs; do not silently replace them with successful responses.
- Preserve the date. Brand availability, pricing and model versions are time-sensitive.
A practical starting design is five or more independent runs per condition, expanded when rankings are unstable. The exact sample size depends on your risk and resources; report it instead of presenting a single memorable response as typical.
Measure more than the first brand named
Extract structured data from every response before interpreting prose. Keep qualitative reasoning analysis separate from measured rankings.
| Measure | How to calculate or code it | Why it matters |
|---|---|---|
| Brand overlap | Intersection of the brands named in two conditions divided by their union, or report the raw intersection for a fixed list length | Shows whether the candidate set changes at all |
| Rank movement | Record each brand’s position; summarize average absolute movement or a rank-correlation statistic | Distinguishes a reordered list from a completely different list |
| Top-choice stability | Count how often each brand appears first across repeated runs | Captures sampling variation within a language |
| Constraint compliance | Code whether each answer respects market, budget, features and availability requirements | Prevents a language from appearing better merely by ignoring constraints |
| Rationale themes | Code reasons such as price, service, repairability, ecosystem or local availability using the same rubric | Reveals changed decision criteria even when brands overlap |
| Uncertainty and caveats | Record explicit limitations, missing data and requests for clarification | Shows whether confidence changes with language |
For a top-three list, report both overlap and position. If one language names Brand A first in four of five runs while another names it first once, that is different from both languages listing the same three brands in a different order. Include confidence intervals or run-level counts when the sample is large enough; otherwise show the raw observations and avoid overstated precision.
Use monolingual, cross-lingual and translated-test conditions
MEGA’s documented vocabulary helps distinguish what you are testing:
Monolingual condition
Examples, instructions and test request are all in the same language. This approximates a user operating entirely in that language and tests end-to-end behavior.
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Zero-shot cross-lingual condition
Prompt examples or instructions are in one language while the test request is in another, without task-specific translation or tuning. This isolates cross-language transfer and can expose whether the model carries decision rules across languages.
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Translated-test condition
Translate a benchmark or a fixed prompt set, then evaluate it in the target language. This is useful for comparability, but translation artifacts can become the measured effect. Have native reviewers inspect equivalence and report the translation process.
Do not collapse these conditions into one score. A language effect that appears only in translated prompts may indicate wording or cultural loss, while an effect that persists in native-authored and explicit-market prompts is stronger evidence of a model behavior.
Have native speakers judge the answers
Automated scoring is useful for extraction, but cultural fit, naturalness and local availability often require human review. PARIKSHA’s 2024 summary reports different agreement levels by evaluation mode and language, stronger human–LLM agreement for pairwise comparison than for direct assessment, lower agreement in some languages and evaluator self-bias as a concern.
Build a blinded review rubric
Give reviewers the intended market and the master specification, but hide the model condition and, where feasible, the language label. Ask at least two independent fluent reviewers to rate:
- Whether the prompt versions preserve the same meaning.
- Whether each recommendation is available and relevant in the named market.
- Whether the rationale uses appropriate local terminology and constraints.
- Whether confidence is justified by the evidence shown.
Resolve disagreements with a predefined rule, such as a third reviewer or adjudication. Keep reviewer comments separate from model-generated rationales so the two are not accidentally treated as the same evidence.
Calibrate automatic judges
Before scoring the full set with another model, create a calibration sample that humans label. Compare the automated judge with those labels by language and task. If agreement drops in one language, report it and use human judgments for the claims that depend on cultural nuance. Pairwise questions such as “which of these two answers better satisfies the specification?” may be more reliable than asking a judge for an unconstrained quality score, but this must be checked on your sample.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and fixes
Changing language and country together
Symptom: Every French run uses France while every English run uses the United States. Fix: Add fixed-market prompts in both languages and a separate language-plus-market matrix.
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Using literal translations only
Symptom: Native reviewers call one prompt unnatural or ambiguous. Fix: Use native-authored versions, back-translation for auditing and an equivalence review before collecting outputs.
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Comparing one response per language
Symptom: A single striking recommendation becomes the headline. Fix: Repeat conditions, report all runs and distinguish within-language variance from between-language differences.
Ignoring retrieval and personalization
Symptom: The system searches different sources or uses a different location for each language. Fix: Freeze tools and location, save retrieved evidence, or treat retrieval and location as explicit experimental factors.
Scoring only brand names
Symptom: Lists look similar, but one language ignores the budget or gives unsupported reasons. Fix: Score constraints, rationale themes, caveats and confidence as separate outcomes.
Letting an automatic judge define cultural fit
Symptom: A judge favors fluent-sounding answers in its strongest language. Fix: Calibrate by language against blinded native-speaker labels and report agreement differences.
Interpret results without overstating them
Use cautious claims tied to the design. “In this model, date range and prompt set, the French and English conditions produced different top-three lists under a fixed Canadian market” is supportable if your records show it. “French speakers prefer Brand A” is not: the experiment measured model outputs, not people’s preferences.
Check four comparisons before drawing a conclusion:
- Between-language difference: Are overlap and rank movement larger than ordinary repeat-to-repeat variation?
- Within-language stability: Does each language produce a consistent pattern, or are both highly variable?
- Locale sensitivity: Does explicitly naming the market reduce the difference?
- Evaluation sensitivity: Do native human ratings agree with automated scores?
Brand availability, prices and market context can change after the test. Preserve the model/version, prompts, settings, retrieved material and date so another analyst can reproduce the comparison. The cited studies do not establish that one brand is globally preferred, nor that any prompt technique eliminates all cross-language differences.
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Publish enough detail for readers to understand exactly what changed:
- Research question and hypotheses, with language and market defined as separate variables.
- Languages, intended market, product category, user profile and constraints.
- Model identifier, system instructions, tools, retrieval sources and sampling settings.
- Prompt text, authorship or translation method, reviewer qualifications and revisions.
- Number and order of runs, seeds, failures and excluded outputs.
- Brand lists, ranks, overlap, constraint compliance and rationale codes for every run.
- Human-review rubric, number of reviewers, adjudication method and agreement by language.
- Date, prices or availability snapshots and all known limitations.
This report makes a language comparison auditable rather than anecdotal. It also lets you rerun the test when a model, market or product catalog changes.
Bottom line for practitioners
Language can change an AI’s recommendations because it may cue an implicit locale, activate uneven multilingual knowledge or be handled with different capability. Current research supports those mechanisms at the level of locale questions and multilingual advice, not a universal law about consumer brands. The defensible way to find out is experimental: hold the market and runtime constant, use natural meaning-matched prompts, repeat each condition, measure rankings and rationales, and validate culturally sensitive judgments with native speakers.
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