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You can test the effect by keeping the product need, target market, model, settings, tools, and date window fixed; changing only the language; repeating every condition; and comparing brand rankings, rationales, and human judgments. The protocol below is designed to separate language effects from accidental changes in wording or market assumptions.
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What can change when the language changes?
Language may imply a locale even when you do not name one
A model may treat language as a clue about where an answer should apply. Google Research’s LocQA study tested questions in which language was the only locale cue. Across 2,156 questions, 12 languages, and 32 models, the study reported a global preference for answers relevant to the United States, even when questions were asked in languages other than English. It also found preferences for larger-population locales when several locales shared a language.
That is evidence of locale-linked behavior, not direct evidence that a certain consumer brand wins in a certain language. A recommendation can shift if the model infers a different market and therefore changes assumptions about availability, prices, regulations, or common use.
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Information and capability are uneven across languages
Brand and product information is not represented equally in every language. A model may have more detailed, recent, or consistent material about a product category in one language than another. Its ability to follow constraints, interpret informal wording, or produce a well-structured answer can also vary by language. These are plausible mechanisms to test, not causes established for any particular brand by the studies summarized here.
Translation is not a perfect control. Microsoft Research identifies the loss of local and cultural nuance in translated benchmarks as a challenge for multilingual evaluation. A literal translation can change politeness, technical terminology, implied audience, or the way a budget and preference are understood.
Advice tasks already show cross-language differences
An ACL 2025 study by Qianying Liu, Katrina Qiyao Wang, Fei Cheng, and Sadao Kurohashi examined multilingual personalized advice for university applications, travel, and relocation. It reported significant differences in scores and in the structure of the reasoning produced for non-English settings. This supports the narrower conclusion that decision-style outputs can vary by language. It does not, by itself, establish that consumer brand rankings will change.
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One striking answer is not a result
Generative systems can vary from run to run. A single answer in one language may contain an unusual brand, an omitted option, or an especially persuasive rationale by chance. A credible comparison therefore measures repeated runs, preserves complete outputs, and reports overlap and rank changes rather than presenting one dramatic example.
What the published evidence does—and does not—show
| Source | What was evaluated | Reported result | How it applies to brand testing |
|---|---|---|---|
| Google Research, LocQA publication page | 2,156 questions in 12 languages evaluated with 32 models; language was used as a locale cue in the study design | The authors report a global bias toward answers relevant to the United States and preferences for larger-population locales when languages were shared | Shows that language can trigger geographic assumptions; it does not test consumer brand rankings |
| ACL 2025 multilingual advice study | University applications, travel, and relocation advice in multiple languages | Scores and reasoning structure differed significantly in non-English settings | Supports testing multilingual decision outputs, while leaving brand preference as a narrower question |
| Microsoft MEGA project documentation | Prompt-based multilingual benchmark evaluation | Defines monolingual, zero-shot cross-lingual, and translated-test setups | Provides useful names for separating evaluation conditions |
| PARIKSHA repository summary, 2024 | 30 models across 10 Indic languages, with 90,000 human evaluations and 30,000 LLM-based evaluations | Agreement varied by evaluation mode and language; pairwise comparison aligned better with people than some direct assessments, and evaluator self-bias was a concern | Supports native-speaker review and validation of automated scoring |
| Microsoft Research multilingual evaluation, 2023 | Seven benchmarks covering reasoning, understanding, and generation | The page reports more than 10 points of average improvement from cross-lingual transfer on arithmetic reasoning and open-domain question answering | Demonstrates that evaluation results can move across languages; the figure should not be generalized to brand recommendation |
The LocQA publication page does not display a publication year, so no year is inferred for that result. The figures above retain the scope reported by each source and should not be treated as measurements of the consumer market.
How to test whether language changes AI recommendations
1. Define the task and the market before writing prompts
Write a short test specification that cannot drift between runs:
- Product category and the exact buying or selection task.
- User need, experience level, and non-negotiable requirements.
- Budget, currency, and any time horizon.
- Intended market, such as a named country or region.
- Number of recommendations requested and the ranking rule.
- Date on which availability, prices, and market conditions are recorded.
Keep the intended market fixed while changing language. Add a separate test arm in which the market is stated explicitly in every language. Comparing an implicit-market arm with an explicit-market arm helps reveal whether a language is causing the model to infer geography.
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2. Create meaning-matched prompts, not just literal translations
Ask fluent speakers to write or review each version. They should confirm that the product need, constraints, tone, and market refer to the same thing. Record regional variants of terminology when they matter. For example, a term for a mobile plan, appliance size, or academic credential can carry different meanings across countries that share a language.
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A useful base template is:
Recommend {{number}} brands for {{product category}} for a {{user profile}} in {{market}}. Requirements: {{constraints}}. Budget: {{amount and currency}}. Explain the ranking, state important trade-offs, and identify any assumption you had to make. Answer in {{language}}.Make a second version that does not name the market, if you are intentionally testing implicit locale cues. Do not silently add a country name to one translation or convert the currency in only one condition.
3. Hold the model and execution environment steady
Use the same model and version, system instructions, conversation history, retrieval sources, tools, and sampling settings. Run the conditions in the same date window where practical. If the production system searches the web, uses an account profile, reads a location signal, or applies a region-specific catalog, log those inputs. A language comparison is uninterpretable if one run had access to different sources or a different location.
Save the exact prompt, system message, model identifier, parameter settings, tool results, retrieved documents, timestamp, and complete response. Do not save only the final brand list; the rationale may reveal that the model changed its assumed market or requirements.
4. Repeat every language and market condition
Run each condition more than once. If the system is stochastic, keep the sampling settings fixed and record the seed when the interface exposes one. If the system is deterministic, repeats still help detect changes caused by retrieval, availability, or other external inputs.
A minimal design has two languages and one explicitly named market, repeated several times. A stronger design adds the same languages with the market omitted, plus a second market stated explicitly. That creates a language-by-locale comparison rather than a simple language-versus-language anecdote.
5. Compare the complete outcome, not only the first brand
For each response, extract:
- Every named brand and its order.
- Any numerical score, percentage, or winner label.
- The stated reasons, trade-offs, and caveats.
- Claims about availability, price, regulation, or local popularity.
- Uncertainty, missing information, and requests for clarification.
Keep ranking analysis separate from qualitative reasoning analysis. A list can remain identical while the rationale changes substantially, or the order can change because one response inferred a different market.
6. Use the three evaluation setups deliberately
The MEGA documentation distinguishes three useful arrangements:
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- Zero-shot cross-lingual: the prompt or examples are in one language while the test request is in another, without translated examples for that test.
- Translated-test: a benchmark or prompt is translated for the target-language test.
For a brand experiment, label which arrangement you used. A change that appears only in a translated-test condition may reflect translation choices; a change that persists with native-authored prompts is stronger evidence of a language-linked effect.
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7. Have native speakers review equivalence and fit
Use independent reviewers who understand the target market and language. Ask them to check whether each prompt preserves the same constraints, whether the answer is natural, and whether the recommendations fit the named market. They should not be told which language or condition you expect to win if that could influence their judgment.
Automated judges require validation. PARIKSHA reports that human–LLM agreement differs by evaluation mode and language, with stronger agreement for pairwise comparison than for some direct assessments and a concern about evaluator self-bias. Calibrate an automated rubric against a sample of native-language human judgments before scoring the full set.
Measurements that make the comparison meaningful
Brand overlap
For each pair of conditions, calculate how many brands are shared. If one condition returns five brands and another returns five, a shared count of four means 80 percent list overlap. State the list length and whether duplicate spellings or parent and sub-brand names were normalized.
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Record the position of every shared brand. You can report the average absolute position change or use a rank-correlation statistic when lists contain the same items. If a brand appears in one list but not another, mark it as absent rather than assigning an invented rank.
Repeat-to-repeat variance
Within each language and market condition, compare repeated runs before comparing languages. A language difference is less persuasive when the same condition produces larger swings than the cross-language difference.
Locale explicitness
Code whether the response names the requested market, assumes a different one, or gives no geographic signal. Compare the implicit-market and explicit-market arms. This directly tests the possibility that language is acting as a hidden locale cue.
Reasoning and caveat changes
Use a separate coding sheet for reasons: price, availability, features, service, regulation, popularity, or another stated factor. Record whether each reason is supported by information available in the test environment. Do not convert a change in wording into a measured ranking change.
Human–automated agreement
Have reviewers score a sample for relevance, factual support, cultural fit, and constraint satisfaction. Compare those scores with the automated judge. Report disagreements by language and evaluation mode instead of relying on one overall average.
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A practical 2-by-2 test design
The following matrix separates language from the market cue. Use the same product task in all four cells.
| Condition | Response language | Market instruction | Question it answers |
|---|---|---|---|
| A | Language 1 | Explicit market | Baseline recommendation with locale fixed |
| B | Language 2 | Explicit market | Does language change the result when locale is held constant? |
| C | Language 1 | Market omitted | What locale does the model infer in Language 1? |
| D | Language 2 | Market omitted | What locale does the model infer in Language 2? |
If A and B differ while both name the same market, the result is consistent with a language-linked effect, although data coverage, wording, and model capability remain alternative explanations. If A and B converge but C and D differ, implicit locale inference is a stronger candidate. If all four vary widely between repeats, first investigate sampling or retrieval instability.
Common test failures and how to fix them
Literal translation changes the task
Symptom: One version sounds unnatural or uses a term with a different regional meaning.
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The market changes invisibly
Symptom: One answer discusses local taxes, retailers, or availability for a different country.
Fix: Name the market and currency in the controlled arm, then retain the implicit-market arm as a separate experiment. Log location signals and retrieval results.
Different tools or dates contaminate the comparison
Symptom: One language receives current web results while another uses an offline model response.
Fix: Keep tools, retrieval sources, model version, and date window identical, or label the arms as different environments rather than attributing the difference to language.
One response is treated as representative
Symptom: A single unusual brand is used as proof.
Fix: Repeat every condition, publish the full run count, and show both list overlap and rank movement.
An automated judge rewards fluent wording
Symptom: A polished answer scores higher even though native reviewers find it culturally unsuitable or factually weak.
Fix: Calibrate the judge with native-language human ratings and use pairwise comparisons when they produce better agreement for your task.
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Brand identity is normalized inconsistently
Symptom: Spelling variants, translated names, parent companies, and sub-brands are counted as different items.
Fix: Define normalization rules before scoring and keep the raw output alongside the normalized dataset.
How to report the result responsibly
A clear report should let another analyst reproduce the comparison and see its limits. Include:
- Languages, regional variants, and intended market.
- Product category, user profile, requirements, budget, and currency.
- Native-authored, translated, or mixed prompt provenance.
- Model name and version, system instructions, tools, retrieval sources, sampling settings, and run dates.
- Number of repeats per condition and the complete prompts.
- Brand overlap, rank changes, repeat variance, locale assumptions, and qualitative rationale codes.
- Human-review method, reviewer language skills, rubric, and agreement with any automated judge.
- Availability and price caveats, since market conditions can change after the test.
Use careful language in the conclusion. “The two tested prompts produced different rankings under these settings” is supported by an experiment. “Speakers of Language 2 prefer Brand X” is a population claim that the experiment cannot establish. The cited multilingual studies likewise do not show that one brand is globally preferred or that a particular prompt technique eliminates all cross-language differences.
What this means for people using AI recommendations
If you are making a purchase or shortlist, ask the system to state its market, currency, availability assumptions, and the evidence behind each recommendation. Repeat the request in the language you normally use and, when the decision matters, in another language you understand. Treat a changed list as a signal to investigate assumptions, not as proof that one language reveals the objectively best brand.
For teams evaluating an AI assistant, language should be a tracked test variable alongside model version, retrieval, location, and date. A well-controlled multilingual test can show where recommendations are stable, where locale assumptions leak into the answer, and where native review is needed before deployment.
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