Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration
arXiv:2607.07895v1 Announce Type: new Abstract: Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages and the high cost of manual annotation in underrepresented cultures. To address this gap, we introduce a cost-efficient human-LLM collaborative annotation framework and apply it to construct EspanStereo, a Spanish-language stereotype dataset spanning multiple Spanish-speaking countries across Europe and Latin America. EspanStereo captures both well-documented stereotypes from prior literature and
The proliferation of Large Language Models (LLMs) and their deployment in diverse linguistic and cultural contexts necessitates addressing biases, making current research into culturally specific datasets critical.
A strategic reader should care because improving LLM neutrality and cultural specificity is vital for broader adoption, ethical AI development, and avoiding algorithmic harm in non-English speaking markets.
The ability to construct robust, culturally nuanced datasets for LLM training in non-English languages shifts the paradigm from English-centric AI development to more inclusive and globally applicable models.
- · AI developers targeting non-English markets
- · Linguistic diversity and cultural preservation in AI
- · Ethical AI research organizations
- · Spanish-speaking AI user bases
- · Bias-prone, English-centric LLMs
- · AI models lacking cultural sensitivity
- · Monolingual AI development approaches
This research provides a methodology to efficiently identify and mitigate cultural biases in LLMs for non-English languages.
The development of high-quality, culturally specific datasets will accelerate the deployment and adoption of AI in diverse global regions, particularly the Spanish-speaking world.
This could foster sovereign AI initiatives in non-English speaking nations, reducing reliance on models trained primarily on Western English datasets.
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Read at arXiv cs.CL