PBBQ: A Persian Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models
This addresses a gap in bias detection for Persian cultural contexts, providing a tool for researchers and developers, though it is incremental as it extends existing bias benchmarking to a new language.
The authors tackled the lack of resources for evaluating social biases in Persian large language models by introducing PBBQ, a benchmark dataset with over 37,000 questions across 16 cultural categories, and found that current LLMs exhibit significant biases and often replicate human bias patterns.
With the increasing adoption of large language models (LLMs), ensuring their alignment with social norms has become a critical concern. While prior research has examined bias detection in various languages, there remains a significant gap in resources addressing social biases within Persian cultural contexts. In this work, we introduce PBBQ, a comprehensive benchmark dataset designed to evaluate social biases in Persian LLMs. Our benchmark, which encompasses 16 cultural categories, was developed through questionnaires completed by 250 diverse individuals across multiple demographics, in close collaboration with social science experts to ensure its validity. The resulting PBBQ dataset contains over 37,000 carefully curated questions, providing a foundation for the evaluation and mitigation of bias in Persian language models. We benchmark several open-source LLMs, a closed-source model, and Persian-specific fine-tuned models on PBBQ. Our findings reveal that current LLMs exhibit significant social biases across Persian culture. Additionally, by comparing model outputs to human responses, we observe that LLMs often replicate human bias patterns, highlighting the complex interplay between learned representations and cultural stereotypes.Upon acceptance of the paper, our PBBQ dataset will be publicly available for use in future work. Content warning: This paper contains unsafe content.