7 Points to T singhua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (2025)

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.

## introduction large language models (llms) have demonstrated remarkable capabilities in multilingual natural language processing (nlp) task execution: understanding, generation, and translation across diverse languages @xcite . beyond these conventional applications, due to their rising reasoning ability, llms are increasingly utilized as inquiry agents, serving a diverse global user base @xcite . llms are widely used for providing personalized advice on real-world topics such as travel planning and career development across multiple languages. despite substantial research attention to the immediate context preferences of llms, signif-figure 1 : chatgpt 3.5 response to university application inquiries in english and chinese. gpt-3.5 exhibits significant inconsistency between different languages. tsinghua university is assigned significantly higher scores in chinese (10/10) than english (7/10), and its disadvantages are dismissed in the reasoning. this study seeks to fill this gap by exploring the multilingual nationality biases of state-of-theart (sota) models, which role as widely used intelligent assistants, in reasoning-based decisionmaking processes. rather than focusing on biased immediate context detection, we investigate how these models behave when tasked with heavy reasoning tasks of offering advice in real-world scenar-ios. as illustrated in figure 1 , when queried about university application recommendations across various countries, chatgpt demonstrates notable inconsistencies between different languages concerning tsinghua university. the response in chinese predominantly emphasizes the advantages of tsinghua university, assigning it a higher rating (10/10, full score) compared to the english response (7/10). recommendations and judgments provided by llms in different languages reveal evidence of nationality bias, wherein llms tend to favor or disadvantage certain groups based on their nationality. the absorption and dissemination of such biases by these models may perpetuate stereotypes, marginalize specific groups, and result in inequitable treatment @xcite . to investigate this phenomenon, we examine three distinct and culturally sensitive tasks where llms are expected to act as universal advisory agents: university application recommendations, travel destination recommendations, and city relocation suggestions. we aim to investigate the patterns of bias in llm-generated recommendations when making decisions on national issues. specifically, we examine how these recommendations vary across different languages, yielding multilingual nationality bias. to quantify the presence of bias, we reformulate the agent's potential nationality bias as a comprehensive assessment problem. specifically, we evaluate how the agent rates the same entity (e.g., a university or city) across different language contexts, hypothesizing that various bias dimensions inherent in llms may influence these ratings. drawing inspiration from psychophysics and decisionmaking studies, we revisit thurstone's law of comparative judgment @xcite , which provides a framework for quantifying subjective preferences through pairwise comparisons. our methodology involves compiling lists of top universities, economically leading cities, and travel destinations, from various countries, forming triplets of options for each task (e.g., the university of tokyo, peking university, and stanford university). we then prompt sota llms to assign numerical scores to each candidate within the triplet, reflecting their recommendation preferences. this process is repeated for hundreds of triplets across multiple languages, enabling us to observe patterns of bias in the agent's scores towards the candidates. two primary research questions are addressed here to guide our investigation: rq1: how do llms exhibit varying bias when acting as agents in providing advice on national issues across different languages? in this study, we observe the overall pattern of score distribution varies markedly across languages. llms display local language biases across different tasks, especially in scenarios such as university application recommendations. edge-cutting models like gpt-4 showcase lower bias when operating in english. however, they show significant bias in non-english languages, which impacts the fairness and consistency of the agent's recommendations. rq2: what role do user demographics and reasoning strategies, such as chain-of-thought (cot) prompting, play in influencing the bias patterns of llms when they act as agents on national issues across different languages? our results highlight that user demographics (gender, language group) and cot play crucial roles in shaping llm bias patterns on national issues. cot does not always mitigate bias, it can amplify disparities, especially in non-english languages. furthermore, bias dynamics vary based on demographic factors, such as gendered speech patterns in different cultures. these findings underscore the need for multilingual bias mitigation strategies that account for both demographic variation and the impact of reasoning strategies like cot. explicating these inquiries provides us with a unique perspective in studying the nationality biases present in multilingual llms when performing complex reasoning-based decision-making tasks. this empirical exploration not only highlights the importance of understanding these biases, but also underscores the need for further research to enhance the personalization and inclusiveness of ai-driven applications across linguistic, educational, and demographic boundaries. bias in llms reasoning agents recent studies have extended bias research beyond immediate context preference to examine complex reasoning and decision-making tasks. several studies have investigated the use of llms as simulations of multilingual survey subjects. @xcite examined llm performance in moral reasoning tasks, particularly in responding to variations of the trolley problem. @xcite explored the subjective global opinions of llms by prompting models to answer binary-choice questions under explicit persona settings in a multilingual context. @xcite further advanced this approach by developing the simulation of synthetic personas, and designing questionnaires based on real-world news to assess biases in model-generated responses. while these studies provide valuable insights into biases in complex reasoning and decision-making tasks under multilingual settings, fall short of providing a comprehensive understanding of real-world applications. other studies addressed tasks such as hiring screening agents @xcite and university application agents @xcite in english. not only their studies limited to english, but they con-strain the models by restricting their ability to engage in chain-of-thought (cot)-like reasoning during responses. this significantly limits the scope and depth of bias analysis in structured decisionmaking processes. ## methodology we begin by formalizing our decision-making tasks as comprehensive evaluation problems, where the goal is to assign overall ratings to entities-such as universities, cities, or travel destinations. this formulation acknowledges that complex advisory tasks are susceptible to multiple sources of bias, including but not limited to linguistic and gender biases. our framework is designed to systematically detect how these different bias dimensions influence the final ratings provided by llms. to empirically evaluate these effects, we simulate real-life advisory scenarios across three domains: (1) an academic career planning advisor assisting with university application decisions, (2) a career planning advisor supporting city relocation suggestions, and (3) a travel planner offering destination recommendations. for each scenario, we generate triplets consisting of three diverse candidate options (e.g., universities or cities) and prompt llms to provide a recommendation along with an analysis and rating that reflects its underlying preferences. by repeating this process across hundreds of triplets in multiple languages, we collect statistical data that allows us to uncover patterns of bias in the model's recommendations. this approach not only highlights the influence of the primary language environment on decision-making but also enables us to assess the impact of additional bias dimensions, such as gender, on the model's evaluations. ## conclusion this study provides the first comprehensive investigation of multilingual nationality bias in state-ofthe-art (sota) large language models (llms) across reasoning-based decision-making tasks. our findings reveal that while llms exhibit lower bias in english, significant disparities emerge in non-english languages. this bias impacts the fairness and consistency of choices and the structure of reasoning. the bias patterns observed are influenced not only by language differences but also by user demographics and reasoning strategies. for example, in non-english contexts, chain-of-thought (cot) prompting often exacerbates rather than mitigates bias, and female-based decisions usually introduce higher bias than male-based ones. furthermore, our evaluation demonstrates that different models prioritize decision-making criteria differently. future research should explore bias mitigation techniques tailored for multilingual settings, considering both linguistic and cultural factors to enhance fairness and inclusivity in ai-driven decision-making applications.