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Same Entity, Different Script: Diagnosing Cross-Script Entity Matching Failures in Language Models

Athira Gopal, Patil Prathamesh Pravin, Ankush Kadu, Arpit Jain, Ashwanth Krishnan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31407 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

يمكن أن تنتج نموذجًا متكاملًا من اللغات ذات الصلة بتحديد الهوية المختلفة عندما يتم كتابة أسماء الأشخاص نفسها في نسخة مختلفة. نحن ندرس هذا التأثير باستخدام 601 زوج من الهوية المختبرة مع أشكال Devanagari وLatin المختبرة، مع الحفاظ على الأسئلة والتقارير المرتبطة. Qwen2.5-7B تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة تساؤلات ذات الصلة

Machine translation (Meta M2M100); the English original follows.

Abstract

Multilingual language models can give different identity judgments when the same people's names are written in different scripts. We study this effect using 601 audited identity pairs with attested Devanagari and Latin forms, holding the question and relation fixed. Qwen2.5-7B's same-person accuracy falls from 35.1% in Latin to 8.5% in Devanagari under Marathi prompts, while different-person accuracy remains above 98%. Evaluation across five models reveals distinct response biases, and reduced tokenization fragmentation does not reliably close the gap. Replicating the design on 374 audited Malayalam pairs built from the same entities reproduces the effect in a second script and language family: same-person accuracy falls to 5.3%, about half the matched Devanagari rate, while the margin still ranks matching above non-matching names at 0.925 AUROC. Latin-trained probes recover label information from Devanagari states, exceeding a string-feature baseline under similarity-matched negatives. Scalar calibration recovers 87% of the raw model-probe balanced-accuracy gap, identifying decision criterion placement as a major contributor. Activation patching localises causal sensitivity to the second entity in Qwen's middle layers; one shared sensitivity direction transfers about half the full-state margin correction. A fact-independent intervention at this site raises held-out Marathi same-person accuracy from 6.5% to 33.5%, with 98.5% different-person accuracy. Together, these results distinguish script-dependent discrimination from criterion placement and identify a causal site for improving identity judgments.

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Topics: Devanagari, Computer science, Marathi, Artificial intelligence, Natural language processing, Telugu

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