  Retrieval-augmented generation (RAG) is becoming an increasingly popular
technique for integrating internal knowledge bases with large language models.
In a typical RAG pipeline, three models are used, responsible for the
retrieval, reranking, and generation stages. In this article, we focus on the
reranking problem for the Polish language, examining the performance of
rerankers and comparing their results with available retrieval models. We
conduct a comprehensive evaluation of existing models and those trained by us,
utilizing a benchmark of 41 diverse information retrieval tasks for the Polish
language. The results of our experiments show that most models struggle with
out-of-domain generalization. However, a combination of effective optimization
method and a large training dataset allows for building rerankers that are both
compact in size and capable of generalization. The best of our models
establishes a new state-of-the-art for reranking in the Polish language,
outperforming existing models with up to 30 times more parameters.
