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ARXIV:2604.19292 · MULTILINGUAL LLMS · SUBMITTED 22 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.19292MULTILINGUAL LLMSSUBMITTED 22 APR · 20:32 UTCFRESHNESS STALEGuy Mor-Lan · Omer Goldman · Matan Eyal · Adi Mayrav Gilady · Sivan Eiger · Idan Szpektor · +3 at arXiv
LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales.
Opportunity summary
Pain LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales. This advancement, however, exposes models to the risk of biased behavior, as knowledge…
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.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Inter-lingually, we show a global bias towards answers relevant to the US-locale, even when models are asked in languages other than English. Code availability…
Multilingual LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales.
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10.48550/arXiv.2604.19292LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales.
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- and intra-lingual biases, via their ability to answer locale-ambiguous questions. To this end, we present LocQA, 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 to LocQA locale-ambiguous questions thus reveal models' implicit priors. We used LocQA to 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 as demographic probability engines, prioritizing locales with larger populations. Taken together, insights from LocQA may help in shaping LLMs' desired local behavior, and in quantifying the impact of various training phases on different kinds of biases.
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Proof status
unverified0 refs; 4 sources; 67% coverage.
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Dimensions overall score 7.0
PROBLEM
LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales. This advancement, however, exposes models to the risk of biased behavior, as knowledge and norms may propagate across language...
METHOD
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.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Inter-lingually, we show a global bias towards answers relevant to the US-locale, even when models are asked in languages other than English. Code availability is flagged in the production record; the pub...
WHY NOW
Multilingual LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 25, "author": "Guy Mor-Lan; Omer Goldman; Matan Eyal; Adi Mayrav Gilady; Sivan Eiger; Idan Szpektor; Avinatan Hassidim; Yossi Matias; Reut Tsarfaty"
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LocQA exposes implicit local and global biases in multilingual LLMs by quantifying their tendency to favor US-centric or demographically larger locales.
Segment
Multilingual LLMs
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7.0/10 public viability
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2/3 checks · 67%
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reason
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proof status
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