S ee GULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes (2024)

Abstract: While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English. This is especially problematic for evaluations targeting inherently socio-cultural phenomena such as stereotyping, where it is important to build multilingual resources that reflect the stereotypes prevalent in respective language communities. However, gathering these resources, at scale, in varied languages and regions pose a significant challenge as it requires broad socio-cultural knowledge and can also be prohibitively expensive. To overcome this critical gap, we employ a recently introduced approach that couples LLM generations for scale with culturally situated validations for reliability, and build SeeGULL Multilingual, a global-scale multilingual dataset of social stereotypes, containing over 25K stereotypes, spanning 23 pairs of languages and regions they are common in, with human annotations, and demonstrate its utility in identifying gaps in model evaluations.

## introduction generative multilingual models @xcite @xcite have gained popular usage in the recent years due to their gradually increased functionalities across languages, and applications. however, there has been a severe lack in cross cultural considerations in these models, specifically when it comes to evaluations of their safety and fairness @xcite . these evaluations have been known to be largely restricted to @xcite , and typically only the english language @xcite . example lang. (country) s o (oaxaqueñas, indígena) (oaxacan, indigenous) es (mexico) 3 2 (ฝรั ่ งเศส, รั กการประท้ วง) (french, love protests) th (thailand) 3 3.0 (lucani, mafiosi) (lucanians, mafia) it (italy) 2 4 (waserbia, ukatili) (serbs, brutal) sw (kenya) 2 3 (corses, belliqueux) (people from corsica, warlike) fr (french) 3 2.33 this is inherently problematic as it promotes a unilateral narrative about fair and safe models that is decoupled from cross cultural perspectives @xcite . it also creates harmful, unchecked effects including model safeguards breaking down when encountered by simple multilingual adversarial attacks @xcite . as language and culture are inherently intertwined, it is imperative that model safety evaluations are both multilingual and multicultural @xcite . in particular, preventing the propagation of stereotypes -that can lead to potential downstream harms @xcite -is crucially tied to geo-cultural factors @xcite . yet, most sizeable stereotype evaluation resources are limited to the english language @xcite . while some efforts have created resources in languages other than english @xcite , they are limited to specific contexts. on the other hand, some approaches such as by @xcite have global coverage of stereotype resources but are restricted to the english language alone. conse-quently, they fail to capture uniquely salient stereotypes prevalent in different languages of the world, as simply translating them to other languages will lose out on cultural relevance @xcite . in this work, we address this critical gap by employing the seegull (stereotypes generated using llms in the loop) approach @xcite to build a broad-coverage multilingual stereotype resource: seegull multilingual. it covers 20 languages across 23 regions of 19 countries they are commonly used in. it contains a total of 25,861 stereotypes about 1,190 identity groups, and captures nuances of differing offensiveness in different global regions. we also demonstrate the utility of this dataset in testing model safeguards. ## dataset creation methodology stereotypes are generalizations made about the identity (id) of a person, such as their race, gender, or nationality, typically through an association with some attribute (attr) that indicates competence, behaviour, profession, etc. @xcite . in this work we create a multilingual and multicultural dataset of stereotypes associated with nationality and region based identities of people. we use the methodology established by @xcite , which is constituted primarily of three steps: (i) identifying relevant identity terms, (ii) prompting a generative model in a few-shot setting to produce similar candidate associations for identity terms from (i), and finally (iii) procuring socially situated human validations for those candidate associations. we chose 20 languages that diversify coverage across global regions (a.1) as well as prevalence in documented llm training datasets @xcite . some languages are used as a primary language in multiple countries with distinct geocultures and social nuances (e.g., spanish in spain and mexico). we consider each language-country pair individually and conduct the following steps separately for each pair. ## dataset: seegull multilingual we introduce the dataset seegull multilingual (sgm), a large scale dataset of stereotypes with broad global coverage. the stereotypes are in the form of (identity term, attribute), and include information such as how frequently they were identified as stereotypes, and their mean offensiveness rating. a snapshot of the data is in coverage: sgm covers stereotypes in a total of 20 languages, as collected from 23 regions across 19 countries of the world. the dataset has a total of 25,861 stereotypes about 1,190 unique identities -including gendered demonyms where applicable -and spread across 7,159 unique attributes. overlap with english seegull: the english seegull (sge) resource from @xcite has approximately 7,000 stereotypes about nationalities. sgm has 9,251 unique nationality based stereotypes, of which, only 949 stereotypes are in common with sge. these 949 unique stereotype occur as a total of 2370 tuples in sgm, present in various languages in different ways, such as (afghans, terrorists) appearing as (afghani, terroristi) in italian, and (afghanen, terroristen) in dutch. the maximum overlap is seen in the spanish dataset collected in spain (13.2%), and portuguese in portugal (13%), while the least overlap was for tamil (4.8%), and hindi (5.37%). 5 additionally, 10,292 regional demonym based stereotypes are all newly introduced in sgm, making the overall dataset overlap with sge about 5%. ## conclusion for holistic safety evaluations of multilingual models, english-only resources or their translations are not sufficient. this work introduces a large scale, multilingual, and multicultural stereotype resource covering a wide range of global identities. it also exposes how these stereotypes may percolate unchecked into system output, due to the prevalent lack of coverage. in considerations of model safety, cross cultural perspectives on stereotypes, their offensiveness, and potential harms must be included. we encourage future work to explore other methods to utilize sgm to measure expressions of representational harms and stereotypes within application-specific contexts for global users.