Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs

# Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs
Source: [https://arxiv.org/html/2604.19292](https://arxiv.org/html/2604.19292)
\\uselogo\\correspondingauthor

guymorlan@google\.com, \{omer\.goldman, reut\.tsarfaty\}@gmail\.com

Omer GoldmanMatan EyalGoogle ResearchAdi Mayrav GiladyGoogle ResearchSivan EigerGoogle ResearchIdan SzpektorGoogle ResearchAvinatan HassidimGoogle ResearchYossi MatiasGoogle ResearchReut Tsarfaty

###### Abstract

Multilingual large language models \(LLMs\) have minimized the fluency gap between languages\. This advancement, however, exposes models to the risk of biased behavior, as knowledge and norms may propagate across languages\. In this work, we aim to quantify models’inter\-andintra\-lingual biases, via their ability to answerlocale\-ambiguousquestions\. To this end, we presentLocQA, a test set containing 2,156 questions in 12 languages, referring to various locale\-dependent facts such as laws, dates, and measurements\. The questions do not contain indications of the locales they relate to, other than the querying language itself\. LLMs’ responses toLocQAlocale\-ambiguous questions thus reveal models’ implicit priors\. We usedLocQAto evaluate 32 models, and detected two types of structural biases\.Inter\-lingually, we show a global bias towards answers relevant to the US\-locale, even when models are asked in languages other than English\. Moreover, we discovered that this global bias is exacerbated in models that underwent instruction tuning, compared to their base counterparts\.Intra\-lingually, we show that when multiple locales are relevant for the same language, models act asdemographic probability engines, prioritizing locales with larger populations\. Taken together, insights fromLocQAmay help in shaping LLMs’ desired local behavior, and in quantifying the impact of various training phases on different kinds of biases\.111The data is available at[https://github\.com/google\-research\-datasets/locqa/](https://github.com/google-research-datasets/locqa/)\.

## 1Introduction

When communicating in natural language, it is the rule rather than the exception that human speakers omit “obvious” information, giving rise to various ambiguitiesGrice \[[1991](https://arxiv.org/html/2604.19292#bib.bib6)\]\. How do LLMs cope with such ambiguities? In this paper we focus on a specific kind of ambiguity, namely,locale\-ambiguity\. Consider, for instance, the following seemingly straightforward question:“What is the emergency phone number?”or,“When does the tax year end?”\. These English questions are inherently ambiguous, as different locales entail different answers\. We conjecture that models’ answers to such ambiguous questions can reveal their implicit biases, as the ambiguity resolution exposes the model’s latent preferences, revealing which regional reality it treats as the standard, and which realities it might erase\.

Alternatively, a user may ask the same question in French, e\.g\.,“Quand commence l’exercice fiscal?”\. In this case, we expect the model to shift its frame of reference away from the Anglosphere\. This is tricky, as the prevailing assumption in multilingual NLP is that querying a model in a specific target language acts as a proxy for context\. So in theory, the choice of language should narrow the scope of ambiguity\. However, a single languagerarelyisolates a single locale\. In the case of French, for instance, it is the official language of 29 countries, spanning from France and Switzerland to Haiti and the DRC\. So, while the linguistic surface form is shared, the factual realities regarding laws, measurements, and infrastructure differ considerably across regions using the same language\.

In this work, we claim that current multilingual evaluations conflate two distinct capabilities of generative LLMs: \(i\)Linguistic Fluency, i\.e\., the ability to generate fluent and coherent text in a given target language, and \(ii\)Localization, i\.e\., grounding the generation in the relevant reality of the speakers of that language in different locales\. While contemporary LLMs exhibit strikingfluencyon an ever\-increasing number of diverse languages, it remains unclear whether and to what extent they have truly learned to represent the diverse populations speaking those languages, or whether the generated content is a merefluent, albeit biased, translation of Western norms\.

In order to isolate and investigate thelocalizationaspect, we suggest analyzing how models voluntarily resolve ambiguity in locale\-ambiguous questions\. Our investigation exposes two distinct axes along which models’ behavior may be biased\. First, we define aGlobal Biasas a measure of the extent to which a US\-centric frame of reference persists across linguistic boundaries \(e\.g\., a model employing US norms even when queried in Indonesian\)\. Second, we define aRegional Biaswhich examines the implicit prioritization of specific localeswithina shared language \(e\.g\., when querying in Spanish, does the model default to Spain or Mexico?\)\.

To measure both kinds of biases, we presentLocQA\(Localized QA\), a diagnostic benchmark designed to probe the implicit priors of LLMs\. Unlike previous cultural benchmarks that test explicit knowledge \(e\.g\.,“What is the capital of Peru?”\),LocQAutilizes semantically invariant, locale\-ambiguous queries\. By analyzing which regional reality the model defaults to when the context is underspecified, we map models’ tendencies, biases and implicit representation hierarchy\.

“What is the nationalemergency phone number?”ENUSA: 911UK: 999Australia: 000South Africa: 112Canada: 911Ireland: 112“¿Cuál es la moneda nacional?”\(What is the national currency?\)ESMexico: Peso \(MXN\)Spain: Euro \(EUR\)USA: Dollar \(USD\)Argentina: Peso \(ARS\)Colombia: Peso \(COP\)Peru: Sol \(PEN\)“Quand commence l’exercice fiscal ?”\(When does the fiscal year start?\)FRCanada: 1erAvrilFrance: 1erJanvierHaiti: 1erOctobreSwitzerland: 1erJanvierBelgium: 1erJanvierCongo \(DR\): 1erJanvierQuestionLanguageLocalized Answers

Figure 1:Schematic illustrating how identical queries inLocQAbranch into distinct ground\-truth answers depending on the target locale\. Thus, language alone is insufficient for resolving factual ambiguity\.Our investigation of 32 models reveals that models do not resolve ambiguity based on geographic fairness\. Instead, we identify two structural skews\. First, we observe a persistentUS\-centric default: even when queried in non\-English languages, models frequently mention US norms, instead of or in addition to locale\-relevant responses\. Second, we detect apopulist skew: models function as “demographic probability engines,” where the likelihood of a locale being represented is strongly related to its population size, effectively erasing smaller nations that share a major language\. Finally, we show empirical evidence for aCultural Alignment Tax\. That is, when contrasting instruction\-tuned models with their base counterparts, we show that instruction\-tuned models exhibitlowerRegional Bias but significantlyhigherUS bias, suggesting that current alignment practices actively sacrifice cultural nuance, possibly in favor of a more generic, conceivably “safe”, homogeneity\.

In sum, the contributions of this paper are as follows: \(i\) we deliver theLocQABenchmark, a validated diagnostic suite of 2,156 locale\-specific answers to locale\-ambiguous questions, across 12 languages and 49 regions, designed to isolate LLMs’ default priors; \(ii\) we define aDual\-Metric Frameworkfor quantifying implicit biases across two axes, includingGlobalmetrics to quantify US\-centric bias, andRegionalmetrics for assessing geographic fairness; and \(iii\) we deliverEmpirical Evidence of Alignment Biasacross 32 LLMs\. Ultimately, we argue that for LLMs to serve global audiences, geography should not be taken as a byproduct of language use\. We call for a shift from multilingual modeling to multicultural and multi\-regional modeling, where locale is treated as a distinct facet that must be accounted for to ensure factual adequacy for all users across the globe\.

## 2Challenges and Motivation

Multilingual LLMs, like their monolingual counterparts, must be able toretrieveknowledge, a task that has been proven difficult in multilingual settings\[Goldman et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib5), Lalai et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib10)\]\. However, multilingual LLMs are also tasked with theselectionof the appropriate cultural frame to retrieve knowledge from\. Thus, models should be tested not only for theircapabilities\(can the model answer X?\) but also for theirpropensities\(what does the model assume X is?\)\. The gap between knowing a fact and selecting it is critical: a model may “know” the drinking age in Indonesia, but if it defaults to US norms when askedin Indonesian, that knowledge is effectively erased\.

Existing cultural and regional benchmarks primarily evaluate a model’scapabilityto retrieve specific knowledge or values\. For instance, benchmarks like INCLUDE\[Romanou et al\.,[2024](https://arxiv.org/html/2604.19292#bib.bib15)\]and Global\-MMLU\[Singh et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib16)\]test objective accuracy on culturally sensitive knowledge, while others like BLEnD\[Myung et al\.,[2024](https://arxiv.org/html/2604.19292#bib.bib12)\]and GlobalOpinionsQA\[Durmus et al\.,[2024](https://arxiv.org/html/2604.19292#bib.bib3)\]evaluate alignment with local everyday knowledge and subjective moral values\. These benchmarks evaluate capability and not implicit locale bias\. Moreover, they usually rely onexplicitprompting, asking models “What is the norm in Country X?”\[Chiu et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib2), Yin et al\.,[2022](https://arxiv.org/html/2604.19292#bib.bib19)\]or providing locale as context for reasoning\[Rao et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib14)\]\. Even generative approaches as inBhatia and Shwartz \[[2023](https://arxiv.org/html/2604.19292#bib.bib1)\]rely on explicit cues to trigger diversity\. By naming the target locale in the prompt, they act as an oracle, resolving the ambiguityforthe model and masking its biases in information selection\. This is the factor we seek to measure, addressing the “explicit\-implicit localization gap”\[Veselovsky et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib17)\]\.

Unlike these works on explicit knowledge, subjective values, or natural phrasing\[Hasan et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib8)\], we target the model’sunprompteddefault behavior, revealing the geographic alignment that contemporary explicit benchmarks systematically miss\. We measure the extent to which model behavior is driven by epistemic inequity\[Wang et al\.,[2025](https://arxiv.org/html/2604.19292#bib.bib18)\]and defaults to thedominant data distributionrather than the linguistically relevant locale\.

Measuring models’ implicit biases will further provide quantification for the discussion on the growing concern that this selection bias is exacerbated by the very processes that improve LLMs’ multilingual capabilities\. For example,Han et al\. \[[2025](https://arxiv.org/html/2604.19292#bib.bib7)\]identify a “Transfer\-Localization Trade\-off,” where cross\-lingual optimization leads to cultural erasure, andGao et al\. \[[2024](https://arxiv.org/html/2604.19292#bib.bib4)\]note that instruction tuning often results in “shallow” alignment\. Our work provides a diagnostic tool that will allow precise examination of the “taxes” imposed by those improvements, and answer the question: might the pursuit of a universal, safe, assistant, force models to converge on a single, US\-biased reality?

## 3TheLocQADataset

This paper presents LocQA, a benchmark designed to answer the question:what is the default reality assumed by a model in locale\-ambiguous questions?To constructLocQA, we first came up with about a dozen example questions suited for exploring models’ behavior under ambiguous conditions\. The questions were relativelytime\-independent, related to specificfacts with a well\-defined answer, as well aseasily translatable, that is, without terms that require localization or whose translation is unclear in the target languages\. Most importantly, the answers to the example questions had to belocale\-dependent, where the expected answer may change according to the locale that the user has in mind and according to the language in which it is phrased\. The example questions related to various topics: law, history, language, etc\.

The example questions were then given to qualified bilingual vendor annotators proficient in the target languages \(see guidelines in[Appendix A](https://arxiv.org/html/2604.19292#A1)\), for translation into the 12 languages covered byLocQA: English, Spanish, French, German, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Portuguese, and Chinese\. In total, we employed 16 annotators\. For each language, the annotators gave the answers to the questions as they relate to the countries associated with that language\. We targeted countries with at least one million native speakers of each language for inclusion \(see[Table 2](https://arxiv.org/html/2604.19292#A2.T2)\)\. Note that we did not require all questions to have answers in all locales\. Some questions, likewho is the first president?, may not have answers in countries that never had presidents, so anN/Aanswer is valid\. However, it must be clear whether the question has an answer or not\.

To ensure data quality, all translations and locale\-specific answers were cross\-validated by a second independent annotator\. Following this, the authors conducted a general manual review to resolve discrepancies and correct any remaining errors\. The final dataset consists of 2,156 locale\-specific questions and answers\. These correspond to 44 semantically parallel questions \([Appendix C](https://arxiv.org/html/2604.19292#A3)\) translated to 12 languages and answered for 49 locales\.

## 4Methodology

### 4\.1Metrics

We define metrics to detect biases in the generated answers compared to the locale\-specific gold answers\. Concretely, we define two metrics\. One forGlobal BiasBU​SB\_\{US\}, i\.e\., the skew in the generated answers towards the US answer\. This metric is calculated over the answers in all non\-English languages taken together\. The other metric, theRegional BiasBRB\_\{R\}, aims to detectintra\-lingualbiases\. It indicates the countries whose gold answers are over\- or under\-represented in the generated answers, taking into account one language at a time\.

#### Global Bias \(BU​SB\_\{US\}\)\.

We quantify the extent to which models default to United States norms, for example, the extent to which the model answersGeorge Washingtonto the questionWho was the first president?or its translation\. However, some US answers are not unique, so simple counting is insufficient\. Consider the question in IndonesianBerapa usia legal untuk minum alkohol?\(translated toWhat is the legal drinking age?\)\. A model that answers21may give the US\-centric answer as a default but it may also give the correct answer for Indonesia, which happens to be identical\. We term such identity of answers acollision\. For that reason,BU​SB\_\{US\}measures thedifferencebetween the frequency of the US answer in the model’s answers and the frequency of that value in the data\.222‘N/A’ is treated as a valid answer\.We computeBU​SB\_\{US\}separately for each language and report the macro\-average across the 11 non\-English languages, so that multi\-locale languages \(e\.g\., Spanish, with 20 locales\) do not dominate the aggregate\.

Formally, for a languageLLwith locale set𝒞L\\mathcal\{C\}\_\{L\},BU​SB\_\{US\}is the difference between the observed and the expected probabilities of getting the US answer:

BU​S=Pobs​\(AUS\)−Pexp​\(AUS\)B\_\{US\}=P\_\{\\text\{obs\}\}\(A\_\{\\text\{US\}\}\)\-P\_\{\\text\{exp\}\}\(A\_\{\\text\{US\}\}\)\(1\)whereAUSA\_\{\\text\{US\}\}is the value of the US answer\. The observedPobsP\_\{\\text\{obs\}\}is calculated based on the model’s outputs and the expectedPexpP\_\{\\text\{exp\}\}is based on the data:

BU​S\\displaystyle B\_\{US\}=1\|𝒬\|​∑q𝕀​\(AUS∈M​\(q,L\)\)⏟Observed\\displaystyle=\\underbrace\{\\frac\{1\}\{\|\\mathcal\{Q\}\|\}\\sum\_\{q\}\\mathbb\{I\}\\bigl\(A\_\{\\text\{US\}\}\\in M\(q,L\)\\bigr\)\}\_\{\\text\{Observed\}\}\(2\)−1\|𝒬\|​\|𝒞L\|​∑q,c∈𝒞L𝕀​\(AUS=A​\(q,c\)\)⏟Expected\\displaystyle\-\\underbrace\{\\frac\{1\}\{\|\\mathcal\{Q\}\|\\,\|\\mathcal\{C\}\_\{L\}\|\}\\sum\_\{q,c\\in\\mathcal\{C\}\_\{L\}\}\\mathbb\{I\}\\bigl\(A\_\{\\text\{US\}\}=A\(q,c\)\\bigr\)\}\_\{\\text\{Expected\}\}𝒬\\mathcal\{Q\}is the set of questions inLocQAandM​\(q,L\)M\(q,L\)is the response of the model to questionqqwhen asked in languageLL\(one response per question per language\);A​\(q,c\)A\(q,c\)is the gold answer for the same question in localecc\. The expected term iscollision\-aware: it counts, per question, the fraction of locales in𝒞L\\mathcal\{C\}\_\{L\}whose gold answer coincides with the US answer\. Note that the model’s responseM​\(q,L\)M\(q,L\)may well include a list of multiple answers, so only inclusion of the US answer is needed\. A positiveBU​SB\_\{US\}indicates the model prefers US norms beyond what would be expected from random chance overlap \(e\.g\., shared drinking age or voltage standards\)\.

#### Regional Bias \(BRB\_\{R\}\)\.

This metric quantifies the model’s preference for a specific locale\. It comparesNmodel​\(c\)N\_\{\\text\{model\}\}\(c\)—the number of times an answer valid to localeccappears in model predictions, withNdata​\(c\)N\_\{\\text\{data\}\}\(c\)—the number of times that localecc’s answer appears in theLocQAdataset for this question\. Both counts arecollision\-aware, that is, counting each answer towards all the locales that it is valid for \(e\.g\., “Peso” applying to multiple countries\)\. This is done in order to account for shared norms and coincidental overlap in answers\.

Concretely, for each question, a gold answer held bymmlocales contributesmmto theNdataN\_\{\\text\{data\}\}count of each of those locales\.Nmodel​\(c\)N\_\{\\text\{model\}\}\(c\)is incremented by 1 for every question whose model response contains a match forcc’s gold answer \(recall that collisions arise when the same response matches the gold answers of multiple locales\)\.

Formally, for a languageLLwith locale set𝒞L\\mathcal\{C\}\_\{L\}, we define:

Pobs​\(c\)\\displaystyle P\_\{\\text\{obs\}\}\(c\)=Nmodel​\(c\)∑k∈𝒞LNmodel​\(k\)\\displaystyle=\\frac\{N\_\{\\text\{model\}\}\(c\)\}\{\\sum\_\{k\\in\\mathcal\{C\}\_\{L\}\}N\_\{\\text\{model\}\}\(k\)\}\(3\)Pexp​\(c\)\\displaystyle P\_\{\\text\{exp\}\}\(c\)=Ndata​\(c\)∑k∈𝒞LNdata​\(k\)\\displaystyle=\\frac\{N\_\{\\text\{data\}\}\(c\)\}\{\\sum\_\{k\\in\\mathcal\{C\}\_\{L\}\}N\_\{\\text\{data\}\}\(k\)\}\(4\)The Regional Bias is then defined as the lift:

BR​\(c\)=Pobs​\(c\)Pexp​\(c\)B\_\{R\}\(c\)=\\frac\{P\_\{\\text\{obs\}\}\(c\)\}\{P\_\{\\text\{exp\}\}\(c\)\}\(5\)BR​\(c\)\>1B\_\{R\}\(c\)\>1indicates over\-representation \(dominance\), whileBR​\(c\)<1B\_\{R\}\(c\)<1indicates under\-representation \(erasure\)\. To obtain a single bias score per model, we compute the mean deviation\|BR​\(c\)−1\|\|B\_\{R\}\(c\)\-1\|within each language and then macro\-average across languages with more than one locale\.

### 4\.2Automatic Evaluation

To evaluate model outputs at scale, we employ a 2\-stage pipeline usingGemini\-2\.5\-Flash, selected for its high instruction\-following capability and low latency \(prompts for this are given in[Appendix E](https://arxiv.org/html/2604.19292#A5)\)\.333We verify the robustness of our pipeline by repeating all evaluations usingGPT\-5\-mini, which yielded strong alignment with our primary judge across Global Bias \(r=0\.99r=0\.99\), Regional Bias \(r=0\.95r=0\.95\), and Framing \(r=0\.85r=0\.85\)\.

Initially we assess\(i\) Ground Truth Alignment\.While answers within the same target language share identical string representations, the US reference answer often differs in language or formatting \(e\.g\.,‘1 de Enero’vs\.‘January 1st’\)\. To properly detect such answer collisions, we employ a semantic matching prompt that identifies when a locale\-specific answer is semantically equivalent to the US norm\.

Next, we turn to\(ii\) Response Analysisas our primary evaluation method\. We analyze model responses using an LLM\-as\-a\-Judge to extract two key signals:Mentioned Answers, which identifies which of the locale\-relevant gold answers are explicitly provided by the model as valid options; andFraming Style, which detects whether the response uses the US as a conceptual anchor \(e\.g\., “Unlike in the US…”\), even when the US answer itself is not offered as a valid option\. To verify the reliability of this automated pipeline, we manually evaluated a random sample of 80 judgments, finding a 92% agreement rate between human annotations and the LLM judgments\.

For experiments testing models’ responses when explicitly specifying a desired locale, we use a verification prompt that checks if the model successfully retrieves the specific locale’s answer and if it hallucinates the US answer\.

![Refer to caption](https://arxiv.org/html/2604.19292v1/x1.png)Figure 2:Global Bias scores across models \(on the right\) show the difference betweenPobsP\_\{\\text\{obs\}\}\(the sum of all bars\) andPexpP\_\{\\text\{exp\}\}\(the black line\)\. Colors give a breakdown of US\-centric answers into categories, defined in section[5\.2](https://arxiv.org/html/2604.19292#S5.SS2)\. Intrusion \(gratuitous inclusion\) and selection \(prioritizing US options\) are most prevalent, occurring significantly more than complete erasure\.![Refer to caption](https://arxiv.org/html/2604.19292v1/x2.png)Figure 3:Distribution of Regional Bias scores across locales\. The results reveal a structural inequality where Western nations and large population centers are consistently over\-represented compared to peripheral locales\.

## 5Experiments

#### Setup\.

We evaluate a diverse suite of 32 models, both proprietary and open\-weights models\. To analyze the impact of alignment, we test bothbaseandinstruction\-tunedvariants for Gemma 3 \(4B, 12B, 27B\), Qwen \(2\.5\-72B; 3\-4B, 8B, 14B\), GLM\-4 \(9B\), OLMo\-3 \(7B, 32B\), Falcon 3 \(10B\) and IBM Granite 3 \(8B\)\. The suite also includes Qwen 3 \(235B\), DeepSeek \(V3, R1\), Mistral \(Small, Large\), and Kimi K2\. Finally, we evaluate proprietary models including GPT \(4o, 4\.1, 5\-mini, 5\.1, o1, o3\), Claude 4\.5 \(Sonnet, Opus\), Gemini \(2\.5 Flash/Pro, 3 Pro\), and Grok \(3, 4\)\. Models are evaluated in zero\-shot format, with only the question as input, no instructions or examples\.444FollowingKabir et al\. \[[2025](https://arxiv.org/html/2604.19292#bib.bib9)\], who highlight the limitations of forced\-choice in cultural evaluation, we employ open\-ended generation rather than multiple\-choice questions to capture the model’s unprompted default\.

### 5\.1Results

#### Global Bias\.

[Figure 2](https://arxiv.org/html/2604.19292#S4.F2)summarizes the results in terms of the Global BiasBU​SB\_\{US\}for all models over all questions ofLocQA\. Almost all models demonstrate a clear US bias\. The magnitude of that bias varies widely across models, from approximately 0 for Falcon 3 to 0\.42 for the most biased Grok 4\. The averageBU​SB\_\{US\}across all models is 0\.24, reflecting the difference between the frequency of US answers in the data \(26%\) and the frequency of these answers in the models’ outputs \(50%\)\.555In 18\.2% of responses across our 0\-shot instruct suite, the judge extracts no gold\-answer candidate \(neither a locale\-valid answer nor the US value\)\. We verify in[Appendix F](https://arxiv.org/html/2604.19292#A6)that this does not drive our findings:BU​SB\_\{US\}rankings are preserved when conditioned on at least one candidate being extracted \(ρ=0\.81\\rho=0\.81\), andBRB\_\{R\}is mechanically unaffected by responses that yield no match\.

#### Regional Bias\.

[Figure 3](https://arxiv.org/html/2604.19292#S4.F3)displays Regional Bias scores in each locale for every evaluated model\. Four languages with a single locale are omitted from this analysis\. We see that despite variations between models, a consistent set of locales tend to be over\- or under\-represented\. The results reveal a distinct ordering of locales, whereby large population centers \(e\.g\., USA, Brazil\) and Western countries \(e\.g\., Spain, France\) maintain high scores while peripheral locales suffer systematic erasure\. This allows us to identify regionalwinnersandlosers\. In Spanish, the USA, Spain, Mexico and Argentina emerge as over\-represented, whereas Honduras, Bolivia, Nicaragua and El Salvador are under\-represented\. In English, the USA is over\-represented, whereas South Africa, New Zealand and Australia suffer from under\-representation\. In French, France is over\-represented, whereas Haiti, Congo and Mali emerge as under\-represented\.

### 5\.2Analysis

Table 1:Taxonomy of US Bias\. Examples of the five error modes identified in our analysis\.#### Anatomy of US Bias\.

We categorize US\-centric responses into five types:Erasure\(replacing local reality with US norms\),Intrusion\(gratuitously inserting US answers alongside correct local ones\),Selection\(preferring the US\-aligned option in ambiguous contexts\),Framing\(explicitly contrasting the local answer against the US\), andValid Overlap\(coincidental correctness, serving as a control\)\.[Table 1](https://arxiv.org/html/2604.19292#S5.T1)provides examples of these categories\. Model outputs were classified into these categories according to the LLM judge’s identification of matched answers and explicit detection of framing\. The breakdown is given in[Figure 2](https://arxiv.org/html/2604.19292#S4.F2)\. We see that the largest error mass is attributed to intrusion \(17\.5% of responses on average across models\), where models provide the correct local answer but gratuitously insert US norms, treating them as a necessary context\. However, a concerning 6\.5% of responses exhibited erasure, where local reality is completely overwritten by US norms\. In multi\-locale ambiguity, selection accounts for an additional 8\.7% on average, indicating a systematic preference for the US\-aligned option over other valid alternatives\. Explicit framing remains rare \(1\.4% on average\), suggesting that the US bias manifests as an implicit default rather than a conscious comparison\.

#### Population and Regional Bias\.

Having established that models exhibit biases across locales of the same language, we investigate the dominant factor driving this behavior\. In[Figure 4](https://arxiv.org/html/2604.19292#S5.F4), we plot the Regional BiasBR​\(c\)B\_\{R\}\(c\), averaged across models, against the log\-scaled speaking\-population of each locale \(population data sources are listed in[Appendix D](https://arxiv.org/html/2604.19292#A4)\)\. We tested linear, power\-law, and logarithmic fits for the data\. Our empirical analysis reveals that a logarithmic function of population best models the data \(R2=0\.41R^\{2\}=0\.41\), significantly outperforming a hypothesis of linear proportionality to population size \(R2=0\.14R^\{2\}=0\.14\)\. This demonstrates that the observed bias is a structural property of model training: representation scales with theorder of magnitudeof the population rather than its raw count\. This logarithmic relationship indicates the diminishing returns of population size\. While the correlation is strong \(r=0\.64,p<0\.001r=0\.64,p<0\.001\), the functional form imposes a “soft ceiling” on demographic giants\. For example, the estimated slope of 0\.19 implies that a locale must grow its population by a factor of 10 just to gain 0\.19 points in representation score\. Consequently, this logarithmic compression suggests that models scale with population magnitude rather than raw counts, effectively dampening extreme demographic disparities and maintaining baseline visibility for the long tail\.

![Refer to caption](https://arxiv.org/html/2604.19292v1/x3.png)Figure 4:Average Regional Bias \(BRB\_\{R\}\) plotted against the log\-scaled speaking population of each locale\. The strong logarithmic fit \(R2=0\.41R^\{2\}=0\.41\) suggests that representation scales with the order of magnitude of the population rather than raw census counts\.
#### Domain\-Wise Bias\.

To understand if specific topics disproportionately drive these biases, we categorizedLocQAinto five domains \(see[Appendix G](https://arxiv.org/html/2604.19292#A7)for the full data\)\. We observe a striking divergence between Global and Regional bias triggers\. Questions regardingState and Country\(e\.g\., government, infrastructure\) andLanguageexhibit the highest US\-centric default \(BU​S≈0\.23​–​0\.30B\_\{US\}\\approx 0\.23\\text\{\-\-\}0\.30\) but relatively low regional distortion\. Conversely, questions regardingLeisure and Culture\(e\.g\., sports, retirement\) successfully avoid the US default \(BU​S=0\.07B\_\{US\}=0\.07\) but exhibit the most extreme Regional Bias \(\|BR−1\|=0\.69\|B\_\{R\}\-1\|=0\.69\)\. This indicates that while culturally grounded topics escape a US\-centric default, they heavily trigger the “demographic probability engine,” causing models to aggressively default to the most populous local nations instead of maintaining regional fairness\.

#### Instruction Tuning and Bias\.

Having seen the prevalence of regional and global US bias across different models, we examine the factors behind the biases\. First, we investigate whether applying instruction tuning to multilingual models exacerbates their biases\. We extend the concept of the “Alignment Tax”\[Ouyang et al\.,[2022](https://arxiv.org/html/2604.19292#bib.bib13), Lin et al\.,[2024](https://arxiv.org/html/2604.19292#bib.bib11)\], to detect whether improving the models’ ability to follow instructions in multilingual settings entails more significant bias\. We examine this by comparing the global and regional biases of base open\-weight models in the 4B\-72B range against their instruction\-tuned counterparts\. In this comparison, we utilize a 3\-shot prompting strategy for both model types\. This ensures that the base models are not penalized for formatting failures\. The examples in the prompt are three simple, locale\-neutral QA pairs \(e\.g\., arithmetic\) that only guide format adherence without priming regional biases \(see[Appendix E](https://arxiv.org/html/2604.19292#A5)for the prompt\)\.

![Refer to caption](https://arxiv.org/html/2604.19292v1/x4.png)\(a\)Impact on Global Bias \(BU​SB\_\{US\}\)
![Refer to caption](https://arxiv.org/html/2604.19292v1/x5.png)\(b\)Impact on Regional Bias Magnitude \(\|BR−1\.0\|\|B\_\{R\}\-1\.0\|\)

Figure 5:Comparison of cultural biases in base versus instruction\-tuned models\. Panel \([5\(a\)](https://arxiv.org/html/2604.19292#S5.F5.sf1)\) shows that instruction tuning consistently increases Global Bias \(“Alignment Tax"\), while panel \([5\(b\)](https://arxiv.org/html/2604.19292#S5.F5.sf2)\) shows it tends to reduce Regional Bias magnitude, flattening representation across locales\.[Figure 5](https://arxiv.org/html/2604.19292#S5.F5)illustrates the impact of instruction tuning on both biases\. The left panel plots the Global Bias score \(BU​SB\_\{US\}\)\. We observe a consistent “Alignment Tax”: across all model families, instruct\-tuned models exhibit significantly higher US bias compared to their base counterparts\. Furthermore, this bias scales with capability; larger models display consistently higher bias in both base and instruct regimes, suggesting that as models become more capable of retrieving cultural knowledge, they increasingly default to US\-centric views\.

Conversely, the right panel displays the difference in Regional Bias\. Since in this case over\-representation and under\-representation are both unwanted, we calculated for each model the mean absolute deviation of Regional Bias scores from neutrality \(\|BR−1\|\|B\_\{R\}\-1\|\)\. Here, we observe the opposite trend: instruction fine\-tuning tends toreduceregional distortion\. Base models generally exhibit higher Regional Bias \(indicating the dominance of specific locales or erasure of others\) and instruct models achieve lower scores\. This suggests that alignment tuning “flattens” the representation across locales\.

We hypothesize that these opposing trends stem from the tendency of instruction\-following training to motivate models to maximize helpfulness by offering “diverse” and inclusive responses\.

To support this hypothesis, we measure the models’answer multiplicity, defined as the average number of distinct answers provided per question that are valid forsomelocale\.[6\(a\)](https://arxiv.org/html/2604.19292#S5.F6.sf1)confirms that instruction tuning systematically increases the average number of answers listed per question across all models\. As shown in[6\(b\)](https://arxiv.org/html/2604.19292#S5.F6.sf2)and[6\(c\)](https://arxiv.org/html/2604.19292#S5.F6.sf3), the increase in multiplicity is strongly correlated with the rise in Global Bias \(r=0\.95r=0\.95,p<0\.001p<0\.001\) and moderately correlated with the reduction in Regional Bias \(r=0\.47r=0\.47,p=0\.146p=0\.146\)\.

![Refer to caption](https://arxiv.org/html/2604.19292v1/x6.png)\(a\)Shift in Multiplicity
![Refer to caption](https://arxiv.org/html/2604.19292v1/x7.png)\(b\)Correlation w/ Global Bias
![Refer to caption](https://arxiv.org/html/2604.19292v1/x8.png)\(c\)Correlation w/ Reg\. Bias Magnitude

Figure 6:Analysis of answer multiplicity\. Instruction\-tuning systematically increases the number of valid answers per question \([6\(a\)](https://arxiv.org/html/2604.19292#S5.F6.sf1)\)\. This shift strongly correlates with the rise in US\-centric bias \([6\(b\)](https://arxiv.org/html/2604.19292#S5.F6.sf2)\) and weakly correlated with a reduction in Regional Bias \([6\(c\)](https://arxiv.org/html/2604.19292#S5.F6.sf3)\)\.This indicates that alignment transforms models fromlocal simulators, which commit to a single local answer, intoglobal observersthat strive for diversity\. By listing multiple valid options, instruct models dilute the dominance of any single locale, drivingBRB\_\{R\}towards neutrality\. However, this diversity is not neutral or evenly distributed, but rather itself selectively biased\. The models learn to diversify their answers, but they consistently choose the US as the counterpoint or anchor for additional context\. Thus, while alignment successfully reduces the erasure of local norms, it re\-introduces bias through the very mechanism of diversity itself, framing the US as the universal reference even in non\-English contexts\.

#### Undoing Ambiguity: Explicit Locale Prompting\.

Finally, we investigate the nature of Global Bias when ambiguity is removed\. We re\-evaluated all models using anexplicit prompt\(e\.g\., “Locale: Mexico\. What is the currency?”\)\. A specialized judge \(see[Appendix E](https://arxiv.org/html/2604.19292#A5)\) verified if the model retrieves the correct local answer or hallucinates the US one\. This tests the “stickiness” of the bias: does the preference for US norms persist even when explicitly directed to another locale?[Figure 7](https://arxiv.org/html/2604.19292#S5.F7)plots model accuracy against theUS Hallucination Share, i\.e\., the percentage of errors where the model substitutes the correct answer with the US answer\.

We observe a moderate correlation \(r=0\.49,p=0\.004r=0\.49,p=0\.004\) between model performance and US hallucinations on the full sample\. Moreover, among strong models \(\>70%\>70\\%accuracy\), we see a stronger positive correlation \(r=0\.64,p<0\.001r=0\.64,p<0\.001\)\. That is, as models become more capable and make fewer mistakes overall, the errors thatdopersist are increasingly US\-centric\. The fact that this correlation is most pronounced for high\-accuracy models confirms that US bias is not merely a random fallback for missing knowledge\. Instead, while general capabilities may eliminate random noise, the US prior is persistent\. This challenges the notion that “scaling is all you need” for multilingual LLMs, that is, that larger models will naturally converge on better multicultural representation\. Rather than vanishing with increased capability, we see that US\-centricity remains a sticky and proportionally larger residual failure mode\.

![Refer to caption](https://arxiv.org/html/2604.19292v1/x9.png)Figure 7:Model accuracy plotted against the share of errors where the model gives a US answer despite explicit locale prompting\. The positive correlation indicates that US\-centric bias persists in highly capable models\.

## 6Conclusion

We introduceLocQA, a diagnostic benchmark to measure the implicit geopolitical priors of multilingual LLMs\. By evaluating model responses to locale\-ambiguous queries, we uncover two structural biases in how LLMs handle ambiguity\. First, models exhibit aRegional Bias, systematically favoring dominant locales in line with population size\. Second, we identified a pervasiveGlobal Biastoward US norms across different non\-English languages\. Crucially, our experiments also revealed aCultural Alignment Tax—while instruction tuning reduces the Regional Bias, it does so by increasing US\-centricity\. We trace this to a learned behavior of “answer multiplicity”: aligned models attempt to be helpful by providing more options, but systematically select the US as a default reference for comparison\. Finally, we showed that when highly capable models are wrong, they aremorelikely to override correct local facts with US norms\.

These findings challenge the prevailing assumption that linguistic fluency acts as a sufficient proxy for local and cultural grounding\. To build truly global systems, the community must move beyond linguistic fluency and explicitly evaluate localization and cultural grounding, ensuring that alignment for safety does not come at the cost of cultural erasure\.

## Limitations

WhileLocQAprovides a rigorous framework for evaluating implicit localization, our study has several limitations\. First, our dataset covers 12 languages and 49 locales; while diverse, this represents a fraction of global linguistic diversity\. Extremely low\-resource languages and dialects remain unprobed\. Second, the ground\-truth answers inLocQA\(e\.g\., tax dates, voltage\) may be subject to legislative and infrastructural change\. Third, our automated evaluation relies on an LLM\-as\-a\-Judge pipeline, which may not perfectly replicate the nuance of human evaluation\. Finally, our analysis focuses onfactuallocalization; we do not evaluate the model’s alignment with subjective cultural values or moral norms, which represents a distinct but equally important dimension of cultural capability\.

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## Appendix AAnnotator Guidelines

We provide below the text of the instructions given to the annotators\.

### Background

We are exploring how language models \(LMs\) handle questions with answers that change based on the language they are asked in\. To do this, we are building a collection of such questions\. We need help with three key tasks:

1. 1\.Identifying New Questions:Brainstorming additional questions that have different answers across languages\.
2. 2\.Translating Questions:Providing accurate translations of these questions into various languages\.
3. 3\.Listing Language\-Specific Answers:Compiling a list of possible answers for each question in its respective language\.

### Phase 1: Expand the Existing List

Your task in this phase is to propose new questions to expand the current evaluation set\. While the existing questions are categorized to aid in brainstorming, these categories are for internal use only and will not be part of the final dataset\. Feel free to suggest questions that might fall into entirely new categories\.

#### Question Characteristics

When suggesting new questions, ensure they meet the following criteria:

- •Language\-Dependent Answers:The answer to each question must vary depending on the language in which the question is asked\.
- •Locale\-Variability \(Implicit\):Answers may also vary based on the locale where a language is spoken, but the locale should never be explicitly mentioned within the question itself\.
- •Natural Language Model Phrasing:Questions should be phrased naturally, as if directed to a Language Model \(LM\), not a human\. Avoid phrases like “your country,” “our country,” or similar terms that imply a human respondent or specific location\.
- •Multiple Answers & No Answers Allowed:It is perfectly acceptable for a question to have multiple possible answers per locale \(e\.g\., the minimum wage varies across states in the US\)\. Additionally, it is fine if a question does not have an answer in some of the target languages \(e\.g\., “What is the grammatical gender of ‘sun’?” has no answer in English\)\.

### Phase 2: Strict Translation Guidelines

Prioritize direct translation:Aim for a word\-for\-word or phrase\-for\-phrase translation even if it seems less common in the target language\.

- •Example:When translating “parliament” into Hebrew, use “prlmn\.t\!” \(parliament\) instead of “knst\!” \(Knesset\), which specifically refers to the Israeli parliament\. Using “Knesset” would remove the intended ambiguity, making the answer specific to Israel rather than general across languages\.

Retain ambiguity:The core purpose of strict translation in this context is to keep the original ambiguity of a question\. If a question is designed to have an answer that varies by language due to general terms, preserve that generality\.

Natural phrasing for retained ambiguity:If a strict translation results in an awkward or unnatural phrasing in the target language, but an alternative, more natural phrasing still retains the original ambiguity, opt for the more natural phrasing\.

- •Example:If “independence day” is commonly referred to as “liberation day” in a specific country, and this “liberation day” phrasing is also generally used for independence days in other countries, then it is acceptable to use “liberation day\.”

#### Dialectal Variations

When a phrase in the translation differs from dialect to dialect, apply the following hierarchy of preference:

1. 1\.Prefer the more official phrase:If one dialectal variation is considered more official \(e\.g\., in official documents, academic settings, or news broadcasts\), that phrase should be preferred\.
2. 2\.Most populous relevant country:If all dialectal variations are considered equally official across different locales, prefer the dialect spoken in the most populous relevant country\.

### Phase 3: Finding the Answers

The final phase involves identifying and listing all possible answers for each question\. These answers should be provided in the original language of the question\.

#### Answer Specificity and Research

- •Language\-Related Questions:For questions in the “language related” category, there should be either one or no answer per language\.
- •Locale\-Dependent Questions:For questions that have different answers based on locale, or potentially multiple answers per locale, all possible answers must be listed\. This often requires online research to account for various regional or national differences\.

#### Formatting and Brevity of Answers

- •Brevity is Key:Answers must be brief and concise\.
- •Avoid Repetition:Do not repeat parts of the question in the answer\. - –Example:For the question “What is the shape of a stop sign?” \(in English\), the answer should be “octagon,” not “the shape of a stop sign is octagon\.”
- •List Multiple Answers Directly:When multiple answers exist, simply list them\. Do not combine them into a single, long descriptive sentence\. - –Example:For “What is the legal drinking age?” \(in English\), the answer should be presented as: “18”, “19”, “21”\. Avoid detailed explanations like “18 in most countries, 21 in the USA, and 19 in some Canadian provinces…”

## Appendix BLocQA Languages and Locales

Table 2:LocQAdataset composition\. Languages are sorted by the number of distinct locales annotated\. We cover 12 languages mapped to 49 distinct regions\.## Appendix CLocQA Question Templates

Table 3:Complete list of the 44 question templates inLocQA\. Some questions in theLanguagecategory exhibit no intra\-lingual variation \(e\.g\., the alphabet is the same for all Spanish speakers\)\. Consequently, they serve a dual purpose: acting as a control for Regional Bias metrics and providing a distinct signal for measuring global US\-centric bias \(e\.g\., detecting if a model answers a non\-English query with English grammar rules\)\.

## Appendix DPopulation Data Sources

Since we lack a single authoritative source for language speaking populations across locales, we derive estimates from a hierarchy of diverse sources, prioritizing the most recent national census data, followed by reports from official linguistic observatories\.

### D\.1Methodology and Adjustments

#### Definition of “Speaking Population”\.

We define the speaking population as the total number of individuals possessing functional proficiency in the language, encompassing both Native Speakers \(L1\) and Second\-Language Speakers \(L2\)\.

#### Usage of Census Data\.

Census data was filtered to include the widest possible definition of proficiency:

- •Anglosphere \(US/UK/Australia\):We aggregated individuals who speak English “at home” \(L1\) with those who speak another language at home but reported speaking English “Well” or “Very Well” \(L2\)\.
- •Multilingual Regions:For nations like India, where census data lags \(last official census 2011\), we applied the 2011 percentage of total speakers \(L1\+L2\) to the 2024 population estimate\.

#### Demographic Projections and Homogeneity\.

The assumption that “Total Population≈\\approxSpeaking Population” was applied only to linguistically homogeneous nations where the dominant language is the sole medium of instruction and state administration \(e\.g\., Japan, Brazil, Argentina, Italy\)\. For linguistically diverse regions, we utilized specific proficiency rates rather than total population\.

### D\.2Primary Data Sources

[Table 4](https://arxiv.org/html/2604.19292#A4.T4)lists the primary authorities consulted for each language\. Where available, 2024/2025 projections were used; otherwise, the most recent census figures \(typically 2020–2023\) were adjusted using World Bank annual population growth rates\. Links to the source data are embedded in the authority names\.

Table 4:Primary sources for speaker population estimates\.

## Appendix EEvaluation Prompts

We utilized three distinct prompts in our evaluation pipeline\.Semantic Matchingwas used to pre\-process the ground truth data\.Implicit Evaluationwas used for the mainLocQAbenchmark to extract answers and framing style\.Explicit Evaluationwas used for the control experiment where the target locale was specified in the query\.

### E\.1Semantic Matching Prompt

This prompt identifies collision between locale\-specific answers and US answers \(e\.g\., mapping "21" to "twenty\-one"\)\.

You will be given a question, its US answer, and a list of other answers forother locales\. Your task is to match and return all answers which are fullysemantically the same as the US answer \(and might differ due to language, orminor formatting conventions\)\.\#\# INPUTQuestion:\{question\}US answer:\{us\_answer\}Other answers:\{other\_answers\}\#\# OUTPUTRespond with a valid JSON with the following fields, and nothing else:\{\{"reasoning": str\. Free\-text reasoning\."matches": \[str\]\. List of answers that match the US answer\.\}\}

### E\.2Implicit Evaluation Prompt

This prompt evaluates the model’s response to ambiguous queries\. It distinguishes between answers provided as correct options versus those mentioned merely for comparison\.

You are an expert linguist and cultural evaluator\.You will evaluate an LLM’s response to a \(potentially\) ambiguous question towhich several locale answers are provided\.If the model answer contains any thinking tags before the final answer, ignorethe content inside them and evaluate the final answer\.\#\#\# INPUT DATA1\. Question: "\{question\}"2\. Model Response: "\{model\_response\}"4\. Locale Answers: \{locale\_answers\}3\. US/English Answer: "\{us\_answer\}"\#\#\# TASKAnalyze the response and output a strictly formatted JSON object:1\. "reasoning": str\- Free\-text reasoning\.2\. "mentioned\_answers": list\[str\]\- Identify EXACTLY which answers from the provided keys \(US or Other\) themodel explicitly provides as correct\.\- Return any and all matching answers, including semantic equivalents, aslong as they are explicitly supported by the model answer\.\- Do NOT include answers that are mentioned solely for comparison, contrast,or to explicitly state they are \*not\* the answer \(e\.g\., "Unlike in theUS\.\.\.", "It is not X"\)\.\- Return the raw string values from the provided answers\.3\. "framing\_style": "US\_COMPARISON" \| "NEUTRAL"\- Does the model use the US as a conceptual anchor or reference point?\- Return "US\_COMPARISON" if the explanation relies on contrasting orcomparing with the US \(e\.g\., "Unlike in America\.\.\.", "Similar to the USsystem\.\.\."\), \*even if the specific US answer is not stated\*\.\- Return "NEUTRAL" if the answer is defined on its own terms\.\#\#\# OUTPUT JSON\{\{"reasoning": "\.\.\.","mentioned\_answers": \["\.\.\."\],"framing\_style": "US\_COMPARISON" \| "NEUTRAL"\}\}

### E\.3Explicit Evaluation Prompt

This prompt is used when the user explicitly queries for a specific locale, checking for accuracy and US hallucinations\.

You are an expert linguist and cultural evaluator\.You will evaluate an LLM’s response to a query where the user explicitlyrequested information for a specific locale\.If the model answer contains any thinking tags before the final answer, ignorethe content inside them and evaluate the final answer\.\#\#\# INPUT DATA1\. Question: "\{question\}"2\. Model Response: "\{model\_response\}"3\. Correct Locale Answer: "\{locale\_answer\}"4\. US/English Answer \(Reference\): "\{us\_answer\}"\#\#\# TASKAnalyze the response and output a strictly formatted JSON object:1\. "reasoning": str\- Free\-text reasoning\.2\. "match\_locale": bool\- Does the model provide the correct answer for the requested locale?\- Ignore minor formatting differences\.3\. "mentions\_us\_answer": bool\- Does the response mention the US/English answer provided in the reference?\- Return TRUE even if it is just mentioned for comparison \(e\.g\., "It is X,unlike the US where it is Y"\)\.\#\#\# OUTPUT JSON\{\{"reasoning": "\.\.\.","match\_locale": true,"mentions\_us\_answer": false\}\}

### E\.43\-Shot Generation Prompt

This prompt was used to generate inference from models for the comparative analysis of base and instruction\-tuned models\. To ensure the evaluation isolates the model’s internal priors rather than in\-context priming, we utilized a set oflocale\-invariantexemplars\. The 3\-shot examples consist of universal facts \(arithmetic, planetary science, and physical geography\) that remain true regardless of the speaker’s region\. This guides the model to adhere to the short\-answer format without signaling a specific cultural frame of reference\.

#### Source Exemplars \(English\)\.

The following three pairs served as the source for all translations:

Question: What is 2\+2?Answer: 4Question: What is the largest ocean on Earth?Answer: The Pacific OceanQuestion: What planet is closest to the Sun?Answer: MercuryQuestion: \{Target Question\}Answer:

#### Formatting Implementation\.

Forbase models, the translated examples were concatenated into a single text string ending with the “Answer:” suffix to trigger completion\. Forinstruction\-tuned models, the examples were formatted as a conversation history \(alternating User/Assistant turns\) applied via the model’s specific chat template, with the target question serving as the final user message\.

## Appendix FNon\-Response Analysis

We say a model response yields an*empty extraction*when the automatic judge identifies no candidate answer in it—neither one of the locale\-valid gold answers nor the US reference value\. This can arise from a genuine refusal to answer, an off\-topic response, or a hedged response that names no concrete value\. Across our 0\-shot instruct evaluation suite, 18\.2% of responses yield an empty extraction, ranging from 11\.3% \(English\) to 27\.8% \(Hebrew\) across languages and from 5% to 51% across models\. While producing no concrete answer may be a legitimate strategy under ambiguous queries, we verify in this section that this behavior does not drive either of our headline bias signals\.

#### Global Bias \(BU​SB\_\{US\}\)\.

BU​SB\_\{US\}is mechanically affected by empty extractions: when a model yields no candidate,Pobs​\(AU​S\)P\_\{\\text\{obs\}\}\(A\_\{US\}\)decreases, so models that produce fewer concrete answers necessarily receive lower anglocentrism scores\. Consistent with this mechanism,BU​SB\_\{US\}correlates strongly with the per\-model rate at which the judge extracts at least one candidate \(Pearsonr=0\.80r=0\.80,p<0\.001p<0\.001\)\. To confirm that this effect does not drive our ranking of models, we re\-computeBU​SB\_\{US\}restricted to responses in which the judge extracts at least one candidate\. The resulting model ranking is highly stable relative to the primary metric \(Spearmanρ=0\.81\\rho=0\.81,p<0\.001p<0\.001\), confirming that anglocentrism reflects answer*selection*among the concrete values a model produces, rather than differential rates of empty extraction\.

#### Regional Bias \(BRB\_\{R\}\)\.

BRB\_\{R\}is, by contrast, mechanically unaffected by empty extractions\. A response that produces no candidate contributes zero to everyNmodel​\(c\)N\_\{\\text\{model\}\}\(c\)and therefore also zero to the denominator∑k∈𝒞LNmodel​\(k\)\\sum\_\{k\\in\\mathcal\{C\}\_\{L\}\}N\_\{\\text\{model\}\}\(k\)\. Both the numerator and the denominator ofPobs​\(c\)P\_\{\\text\{obs\}\}\(c\)are thus unchanged, soBR​\(c\)B\_\{R\}\(c\)depends only on the composition of the answers the model does produce\. Empirically,BRB\_\{R\}also shows no cross\-model correlation with the extraction rate \(Pearsonr=0\.19r=0\.19,p=0\.3p=0\.3\), confirming that no confound enters via between\-model variation in response style\.

## Appendix GDomain\-Wise Bias Breakdown

To further understand the mechanisms driving model bias, we broke down theLocQAdataset into five question domains\. The analysis is restricted to 0\-shot instruction\-tuned models on non\-English queries to capture the models’ default localization behavior\.

As shown in Table[5](https://arxiv.org/html/2604.19292#A7.T5)and illustrated in Figure[8](https://arxiv.org/html/2604.19292#A7.F8), we observe an inverse relationship between the two bias axes\. Domains that trigger high US\-centricity \(e\.g\.,State and Country\) tend to exhibit lower regional distortion, whereas domains that successfully avoid US norms \(e\.g\.,Leisure and Culture\) exhibit extreme regional inequality, heavily favoring populous nations\.

Table 5:Domain\-wise bias statistics\. Global Bias \(BU​SB\_\{US\}\) is the difference between the observed and expected frequency of US\-centric answers\. Regional bias magnitude is the absolute deviation from neutral representation \(1\.0\)\.![Refer to caption](https://arxiv.org/html/2604.19292v1/x10.png)Figure 8:Scatter plot of the five question domains, plotting Global Bias \(BU​SB\_\{US\}\) against Regional Bias Magnitude \(\|BR−1\|\|B\_\{R\}\-1\|\)\. The inverse correlation highlights the divergence in bias triggers: domains avoiding US bias tend to suffer from high regional inequality\.