  Warning: this work contains upsetting or disturbing content.
  Large language models (LLMs) tend to learn the social and cultural biases
present in the raw pre-training data. To test if an LLM's behavior is fair,
functional datasets are employed, and due to their purpose, these datasets are
highly language and culture-specific. In this paper, we address a gap in the
scope of multilingual bias evaluation by presenting a bias detection dataset
specifically designed for the Russian language, dubbed as RuBia. The RuBia
dataset is divided into 4 domains: gender, nationality, socio-economic status,
and diverse, each of the domains is further divided into multiple fine-grained
subdomains. Every example in the dataset consists of two sentences with the
first reinforcing a potentially harmful stereotype or trope and the second
contradicting it. These sentence pairs were first written by volunteers and
then validated by native-speaking crowdsourcing workers. Overall, there are
nearly 2,000 unique sentence pairs spread over 19 subdomains in RuBia. To
illustrate the dataset's purpose, we conduct a diagnostic evaluation of
state-of-the-art or near-state-of-the-art LLMs and discuss the LLMs'
predisposition to social biases.
