SHADES : Towards a Multilingual Assessment of Stereotypes in Large Language Models (2025)

Abstract: Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advancement of LLMs in multilingual settings. In this paper, we introduce a new multilingual parallel dataset SHADES to help address this issue, designed for examining culturally-specific stereotypes that may be learned by LLMs. The dataset includes stereotypes from 20 regions around the world and 16 languages, spanning multiple identity categories subject to discrimination worldwide. We demonstrate its utility in a series of exploratory evaluations for both “base” and “instruction-tuned” language models. Our results suggest that stereotypes are consistently reflected across models and languages, with some languages and models indicating much stronger stereotype biases than others.

## introduction large language models (llms) are a class of artificial neural network trained on large-scale datasets, 2 predominantly concentrated in english @xcite . recently llms with broad use include llama2 @xcite and mistral @xcite . these models and similar have been shown to produce evaluation results comparable to humans on benchmark datasets for a range of english natural language processing (nlp) tasks. this has further spurred the development of multilingual models trained on multilingual datasets, such as llama3 @xcite and qwen2 @xcite . the large-scale datasets used to train llms primarily consist of text written by people, reflecting their personal positions and views @xcite . this includes implicit and explicit social biases about age, gender, race, and other personal identity characteristics, as well as norms and systemic patterns of discrimination @xcite . these are expressed as stereotyped judgements, negative generalizations, toxic language, and hate speech @xcite @xcite . in turn, models trained on such data are prone to propagate such social biases @xcite . stereotypes play a central role in fostering prejudice and discrimination @xcite , and exposure to stereotypes influences perception and behavior @xcite , motivating the need for tools that directly address the propagation of stereotypes in llms. acknowledging the gravity of stereotypes encoded in llms, researchers have developed some methods to identify their generation (e.g., @xcite . however, the vast majority of resources are developed for english @xcite , limiting the ability to address problematic generalizations encoded from languages other than english. the lack of resources, especially parallel ones, also makes it impossible to understand multilingual stereotype effects, such as how negative identity representations may bleed into other languages modeled by the same llm and so influence societal perceptions. our work contributes to this need for resources by presenting shades: a multilingual dataset of stereotypes written by native and fluent speakers 2 currently, "large-scale" may refer from multiple terabytes of text data to billions of tokens @xcite . our data elicitation procedure captures dataset creators' knowledge of the different ways to express stereotypes in the languages they speak and regions where they've spoken it, such as through prescriptive language-e.g., "women should have fun"-and judgements on people's behaviors-e.g., "men who drive are not serious people". annotations of cultural applicability of stereotypes support multilingual bias evaluation and analyses. for instance, the stereotype that "kids are pure at heart," originally added to the dataset in hindi, is labeled as a declarative age stereotype valid in over 30 regions around the world. as such, shades is developed to support multilingual, multicultural, and multigeographical analyses of stereotypes, functioning as a resource in its own right and constructed to aid in bias and stereotype evaluation of llms. languages and regions covered are provided in figure 1 and given the diversity of content, there are many possible applications of shades for the exploration and measurement of stereotypes in llms. here, we present proof-of-concept evaluations to audit thirteen multilingual llms: 8 "base" models from 4 model familes and 5 "instruct" models (4 from the same model families), fine-tuned for dialogue. contributions. in summary, our work makes the following primary contributions: details on the specific contributions of all authors is provided in appendix j, following the credit system @xcite . ## stereotypes and llms following the foundational work of @xcite , the nlp community increased research on the issue of social biases (such as stereotypes) 5 all annotators are included as authors on the paper. encoded in neural networks. many efforts have focused on assessing and mitigating stereotypes and other forms of biases in llms (e.g., @xcite @xcite @xcite @xcite . the importance of this work is also reflected in recent regulatory developments around artificial intelligence (e.g., the european ai act, foot_2 the blueprint for an ai bill of rights foot_3 ), which seek to limite harmful societal outcomes and ensure that ai systems conform to existing regulation (e.g., on gender discrimination). the broader picture: ai safety and ethics. our work on assessing stereotypes is embedded in the larger context of safe and ethical ai (e.g., @xcite @xcite , where researchers focus on issues such as stereotypes and fairness in multimodal models (e.g., @xcite , model toxicity (e.g., @xcite , multicultural value encoding (e.g., @xcite @xcite and value misalignment (e.g., @xcite . approaches to addressing these issues include redteaming (e.g., @xcite , synthetic data generation @xcite , and rlhf @xcite , which benefit from detailed resources on how stereotypes are expressed across different languages. ## dataset design curating a dataset that maintains both crosslinguistic and geographic validity is a large undertaking that requires balancing considerations on annotator expertise, data scope, and engineering requirements, amongst other aspects. in this section, we highlight our processes and decisions that collectively resulted in shades. throughout, we used a consensus-building approach to guide development. further details are provided in the appendix, sections a through e. ## applying the dataset: evaluation we explore multiple evaluation paradigms using the dataset. these are constructed as initial studies to provide examples of using shades to assess and evaluate the stereotypes encoded in multilingual llms, and are not intended to be exhaustive. 13 ## conclusion we have presented shades, a new parallel multilingual dataset of stereotypes in 16 languages, developed for the evaluation of stereotype biases in large language models. creating a dataset of annotated, culturally-specific stereotypes, translated across multiple languages, involves international coordination on sensitive issues and working through nuanced language differences. it also requires developing strategies based on weighing risks and benefits: sharing stereotypes for benchmarking can amplify negative generalizations in languages that may require additional data protection and shepherding. 21 created with consent and care, a dataset focused on stereotypes and societal biases provides a multilingual and multicultural resource grounded in the usage of llms. this can be used to explore, measure, and mitigate the contribution of bias and stereotypes in the content these models produce, which is currently widely consumed. this work leaves open many avenues for future development and research. on the dataset side, shades can be expanded to account for more stereotypes, languages, and regions (such as sub-saharan africa), and the template slot categories may be further refined to account for richer crosslingual variation. future work might explore the application of the templates to generate new instances for evaluation. on the evaluation side, the brief analyses provided here suggest that the dataset can be effectively used to probe and evaluate llm stereotyping behavior.