Multilingual Large Language Models Leak Human Stereotypes across Language Boundaries (2025)

Abstract: Multilingual large language models have gained prominence for their proficiency in processing and generating text across languages. Like their monolingual counterparts, multilingual models are likely to pick up on stereotypes and other social biases during training. In this paper, we study a phenomenon we term “stereotype leakage”, which refers to how training a model multilingually may lead to stereotypes expressed in one language showing up in the models’ behavior in another. We propose a measurement framework for stereotype leakage and investigate its effect in English, Russian, Chinese, and Hindi and with GPT-3.5, mT5, and mBERT. Our findings show a noticeable leakage of positive, negative, and nonpolar associations across all languages. We find that GPT-3.5 exhibits the most stereotype leakage of these models, and Hindi is the most susceptible to leakage effects.

## introduction large language models (llms) are trained on existing language data that encode prevailing social norms and conventions. monolingual language models have been shown to replicate such social stereotypes. @xcite @xcite . multilingual large language models (mllms) are pre-trained on extensive datasets spanning multiple languages, enabling them to perform natural language processing (nlp) tasks in different languages as well as cross-lingual tasks. although many studies have examined western stereotypes in english language models (e.g. @xcite @xcite , research on stereotypes in multilingual models remains limited (e.g. kaneko et al., * both authors contributed equally to this research. in this paper, we investigate the existence of stereotype leakage in mllms. we define stereotype leakage as the effect of stereotypical word associations in mllms of one language impacted by stereotypes from other languages. we focus on analyzing the presence and impact of stereotype leakages. to do so, we conduct a human study to collect human stereotypes, adopt word association measurement approaches from previous studies @xcite to measure stereotypical associations in mllms, and analyze the strength and nature of stereotype leakage in different languages both quantitatively and qualitatively. recent advancements in mllms have made them increasingly language-agnostic. for instance, models from gpt-family and mbart @xcite can operate without being restricted to a specific language, simultaneously handling input and output in multiple languages. this creates opportunities for what we refer to as stereotype leakage from one culture to another. 1 cultural stereotypes about social groups are shaped based on how these social groups are represented, treated, and discussed within each culture @xcite @xcite . hence, people's stereotypes about groups can be impacted by exposure to products and ideas from outside their own cultures. mllms, being the backbone of many natural language processing (nlp) applications, have the potential to exacerbate this issue by exporting harmful stereotypes across cultures and reinforcing anglocentrism @xcite ). 2 we investigate the degree of stereotype leakage in mllms as a step toward understanding and mitigating this issue in ai systems. we test our hypothesis of significant stereotype leakage across languages in mllms by sampling four languages: english, russian, chinese, and hindi. we choose languages from different writing systems-latin alphabet, cyrillic alphabet, chinese characters, and devanagari script-to enable a comprehensive evaluation of stereotype leakages in mllms. the models we assess are mbert, mt5, and gpt-3.5. based on our findings, all models demonstrate varying degrees of stereotype leakage, which occurs bidirectionally across languages without a dominant directionality. among the models tested, gpt-3.5 exhibits the highest degree of stereotype leakage. importantly, the stereotype leakage includes not only negative stereotypes but also positive and non-polar associations. our study shows that stereotypes in other languages about social groups unfamiliar to those cultures are shaped by the stereotypes present in the native language. this indicates that multilingual language models reflect and propagate cultural stereotypes across linguistic boundaries. ## background and related work assessing multi-cultural biases and stereotypes in multilingual settings is challenging. as noted by @xcite , there is a significant lack of benchmark datasets for measuring multilingual fairness. while many datasets exist in english, simply translating these datasets poses issues due to linguistic and cultural disparities. furthermore, many existing fairness evaluation datasets are rooted in western cultures, resulting in a gap that fails to encompass global cultural perspectives. @xcite also highlighted the difficulty of measuring gender biases in languages with rich morphology 2 anglocentrism is the practice of viewing and interpreting the world from an english-speaking perspective with the prioritization of english culture, language, and values. anglocentrism can lead to biases and neglect of global perspectives and experiences. and gender marking. many studies thus have been devoted to expanding the language boundary to assess the presence and impact of biases in multilingual settings by proposing new measurement approaches and evaluation datasets. @xcite focused on evaluating the multilingual fairness of pre-trained multimodal representations. many studies delve deeply into gender biases in multilingual settings. @xcite focused on word representations, while both @xcite and @xcite investigated gender bias in masked language models, each proposing new datasets for analyses. furthermore, @xcite examined occupational biases within norwegian and multilingual language models, seeking to identify and mitigate these biases. addressing intersectional biases, @xcite mapped biases in sentiment analysis systems across english, spanish, and arabic, proposing a framework to measure these biases effectively. additionally, @xcite extended the crows dataset @xcite of sentence pairs in english for measuring bias in masked language models to the french language. building on this foundation, we investigate the dynamics of stereotype transfer across languages in mllms and how stereotypes from one language influence others within a multilingual model. our findings highlight the role of llms in propagating cultural biases and emphasize the need for strategies to mitigate cross-linguistic stereotype leakage. ## measuring stereotype leakage in mllms in measuring stereotype leakage in mllms, we evaluate how stereotypes from one language (the source language) influence the model's behavior in another language (the target language) due to multilingual training. specifically, we assess how stereotypical word associations in the target language reflect biases originating from other languages. although some stereotypes are learned during monolingual training, our focus is on leakage caused by multilingual training. to investigate stereotype leakage, we define equation 1, where mllm tgt represents the stereotypical word associations produced by mllms in the target language. the variables h en , h ru , h zh , h hi denote human stereotypes in four source languages: english (en), russian (ru), chinese (zh), and hindi (hi). we use this formulation to measure the extent to which stereotypes from source languages (h * ) leak into the target language representations of mllms (mllm tgt ). these four languages were chosen because they do not share orthographic systems and allow us to focus on non-trivial cases of stereotype transfer. mllm tgt and h * are all 32 × 30 dimensional matrices, where 32 is the number of traits and 30 is the number of social groups. specific traits and social groups are explained in detail in section 3.1. each entry in these matrices represents the stereotypical association score between a particular trait and a social group. c is the intercept. to isolate the effect of stereotypes captured solely through multilingual training, we introduce the lm tgt variable, representing stereotypical associations from the target language's monolingual model. similarly, lm tgt is 32 × 30 dimensional matrix. since only monolingual bert models are available for all four languages, we use them as proxies for lm tgt in all mllms. 3 the goal is to estimate how h of each language affects mllm tgt using a mixed-effects model. this model fits a linear regression with traits as the random effect variable, producing coefficients (α and β). the α value shows how much the mllm tgt stereotype score changes when the h score changes. a positive coefficient with a p-value below 0.05 indicates a significant effect. significant effects from non-target language stereotypes suggest potential stereotype leakage to the target language. we focus on mbert @xcite , mt5 @xcite , and gpt-3.5 @xcite . mbert and mt5 are back-end mllms; mt5 offers better multilingual performance, while mbert has more comparable monolingual bert models for the four languages. gpt-3.5 is a stateof-the-art, widely deployed generative model. 4 our selection covers diverse architectures -mbert (transformer-based), mt5 (sequence-to-sequence), and gpt-3.5 (large-scale generative) -to explore stereotype leakage comprehensively. with these, we examine the effect of stereotype leakages in mllms. 5 ## stereotype leakage and its effects in this section, we present the quantitative and qualitative results of our assessment of stereotype leak-age across social groups and languages in mllms. quantitatively, we measure stereotype leakage by examining how stereotypical associations in target language models are influenced by human stereotypes from source languages. mixed-effect models are used to quantify this leakage and identify significant cross-language effects. qualitatively, we explore specific stereotypical associations that leak between languages, examining both positive and negative stereotypes foot_5 . we also consider non-polar associations to provide a comprehensive view of how stereotypes are transmitted across languages. ## conclusion multilingual large language models have the potential to spread stereotypes beyond the societal context they emerge from, whether by generating new stereotypes, amplifying existing ones, or reinforcing prevailing social perceptions from dominant cultures. in our study, we demonstrate that this concern is indeed valid. to do so, we establish a framework for measuring the leakage of stereotypical associations in multilingual large language models across languages. overall, we find that the stereotype leakage occurs bidirectionally meaning that when one language transmits stereotypes to others, it likely receives some stereotypes from other languages as well. we also observe the most stereotype leakage effect within the gpt-3.5 model. within the gpt-3.5 model, we observe the strengthening of positive, negative, and non-polar associations in the model. in addition, our study underscores the role of "native" languages in framing social groups unknown to other linguistic communities. such leakage of stereotypes amplifies the complexity of societal perceptions by introducing a complex interconnected bias from different languages and cultures. in the context of shared groups, stereotype leakage may manifest as the manifestation of stereotypes that were not previously present within the cultural setting of a particular group. for non-shared groups, stereotype leakage can extend the reach of existing stereotypes from the source culture to other cultural contexts. to our knowledge, we are the first to introduce the concept of stereotype leakage across languages in multilingual llms. we propose a framework for quantifying this leakage in multilingual models, which can be easily applied to unstudied social groups. we show that multilingual large language models could facilitate the transmission of biases across different cultures and languages. we demonstrate the existence of stereotype leakage within mllms, which are trained on diverse linguistic datasets. as multilingual models begin to play an increasingly influential role in ai applications and across societies, understanding their potential vulnerabilities and the level of bias propagation across linguistic boundaries becomes important. as a result, we lay the groundwork for advancing both the theoretical comprehension of multilingual models and the practical implementation of bias mitigation in ai systems.