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    <abstract>Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases are limited to immediate context preferences. Cross-language disparities in reasoning-based recommendations remain largely unexplored, with a lack of even descriptive analysis. This study is the first to address this gap. We test LLM’s applicability and capability in providing personalized advice across three key scenarios: university applications, travel, and relocation.We investigate multilingual bias in state-of-the-art LLMs by analyzing their responses to decision-making tasks across multiple languages. We quantify bias in model-generated scores and assess the impact of demographic factors and reasoning strategies (e.g., Chain-of-Thought prompting) on bias patterns. Our findings reveal significant biases in both the scores and the reasoning structure of non-English languages. We also draw future implications for improving multilingual alignment in AI systems.</abstract>
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%0 Conference Proceedings
%T 7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias
%A Liu, Qianying
%A Wang, Katrina Qiyao
%A Cheng, Fei
%A Kurohashi, Sadao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liu-etal-2025-7
%X Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, while studies on risks associated with cross biases are limited to immediate context preferences. Cross-language disparities in reasoning-based recommendations remain largely unexplored, with a lack of even descriptive analysis. This study is the first to address this gap. We test LLM’s applicability and capability in providing personalized advice across three key scenarios: university applications, travel, and relocation.We investigate multilingual bias in state-of-the-art LLMs by analyzing their responses to decision-making tasks across multiple languages. We quantify bias in model-generated scores and assess the impact of demographic factors and reasoning strategies (e.g., Chain-of-Thought prompting) on bias patterns. Our findings reveal significant biases in both the scores and the reasoning structure of non-English languages. We also draw future implications for improving multilingual alignment in AI systems.
%R 10.18653/v1/2025.findings-acl.1355
%U https://aclanthology.org/2025.findings-acl.1355/
%U https://doi.org/10.18653/v1/2025.findings-acl.1355
%P 26430-26442

Markdown (Informal)

[7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias](https://aclanthology.org/2025.findings-acl.1355/) (Liu et al., Findings 2025)

• 7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (Liu et al., Findings 2025)

ACL

• Qianying Liu, Katrina Qiyao Wang, Fei Cheng, and Sadao Kurohashi. 2025. 7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias. In Findings of the Association for Computational Linguistics: ACL 2025, pages 26430–26442, Vienna, Austria. Association for Computational Linguistics.