On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena (2025)

Abstract: Language Models (LMs) have been shown to exhibit a strong preference towards entities associated with Western culture when operating in non-Western languages. In this paper, we aim to uncover the origins of entity-related cultural biases in LMs by analyzing several contributing factors, including the representation of entities in pre-training data and the impact of variations in linguistic phenomena across languages. We introduce CAMeL-2, a parallel Arabic-English benchmark of 58,086 entities associated with Arab and Western cultures and 367 masked natural contexts for entities. Our evaluations using CAMeL-2 reveal reduced performance gaps between cultures by LMs when tested in English compared to Arabic. We find that LMs struggle in Arabic with entities that appear at high frequencies in pre-training, where entities can hold multiple word senses. This also extends to entities that exhibit high lexical overlap with languages that are not Arabic but use the Arabic script. Further, we show how frequency-based tokenization leads to this issue in LMs, which gets worse with larger Arabic vocabularies. We will make CAMeL-2 available at: https://github.com/tareknaous/camel2

## introduction multilingual language models (lms) are playing a crucial role in making ai technology accessible to global communities @xcite . as these communities represent diverse cultural backgrounds, the multilingual challenge for lms does not merely stop at handling different languages @xcite , but extends to capturing cultural nuances @xcite . however, past research has highlighted strong favoritism in lms towards entities associated with western culture when operating in non-western languages, leading to a struggle by lms to adapt to cultural contexts and gaps in their performance between cultures on nlp tasks @xcite . ## camel-2: a parallel benchmark our goal is to investigate whether lms handle entities associated with arab and western cultures differently when tested in arabic vs. english languages ( §3). to do this, we first construct a bilingual version of the entity-centric camel @xcite benchmark for measuring cultural biases in lms and extend its coverage by three times ( §2.2). ## is western bias in lms consistent across arabic and english languages? we start by studying whether lms show the same degree of favoritism for western culture, when operating in arabic vs. english languages. our analyses focus on two aspects: cultural adaptation in text-infilling ( §3.2) and cross-cultural performance on extractive qa and ner ( §3.3). ## on the origin of cultural biases motivated by our observations in §3, we analyze various factors that may cause lms to exhibit more severe western bias in arabic than in english. we first study the relationship between the frequency of entities in pre-training data and lm performance ( §4.1). our findings lead to analysis of the impact of arabic word polysemy on lm biases ( §4.2). we also look at scenarios where entities exhibit lexical overlap with other languages that use the arabic script ( §4.3). finally, we examine the role that tokenization plays in the observed issues ( §4.4). ## related work there has been growing interest in studying the cultural considerations surrounding lms @xcite . this encompasses several aspects that lms have to navigate such as cultural commonsense, where lms must differentiate between the societal norms (e.g., bringing a gift when visiting someone) of different cultures @xcite @xcite @xcite . other works explored 0 10k 20k 30k 40k 50k #arabic tokens in vocabulary 30 40 50 60 qa accuracy llama3.3-70b aya23-35b acegptv1.5-13b jais-13b causal lms 0 20k 40k 60k 80k 100k 120k #arabic tokens in vocabulary 25 50 75 ner f1 xlm-r l camelbert b arabert l marbert b arbert b encoder lms figure 8: average qa accuracy and f1 of lms on onetoken entities vs. the number of arabic tokens in their vocabularies. performance drops greatly as vocabularies get too large, with the best performing lms ( ) having medium arabic vocabulary sizes. another line of work explores the cultural appropriateness of lms by analyzing their behavior when handling entities that exhibit cultural variation such as first names @xcite @xcite @xcite and food dishes @xcite . the study of @xcite revealed performance gaps in lms when handling entities associated with arab vs. western culture in arabic language, where models were performed consistently better on western entities. our work builds on this study, with the aim of pinpointing the origins of such gaps. past research on analyzing the cross-cultural performance of lms have focused primarily on the representation of entities in pre-training data @xcite , demonstrating a struggle of lms to learn knowledge about entities that rarely appear in corpora @xcite . however, those analyses have been restricted to the english language only. different from prior work, we analyze multiple facets that can contribute to western biases in lms beyond pre-training data, focusing on cross-linguistic differences in phenomena exhibited by entities and the impact of subword tokenization. ## conclusion we analyzed a variety of factors that can contribute to entity-related cultural biases in lms. we showed how non-english linguistic phenomena such as word polysemy in arabic, lexical overlaps with other languages, and frequency-based tokenization can cause performance degradation on entities associated with arab culture, leading to perceived western biases in lms. we hope our study lays a foundation of important aspects to consider in building culturally fair multilingual lms.