  We introduce a new benchmark for assessing the quality of text-to-text models
for Polish. The benchmark consists of diverse tasks and datasets: KLEJ
benchmark adapted for text-to-text, en-pl translation, summarization, and
question answering. In particular, since summarization and question answering
lack benchmark datasets for the Polish language, we describe their construction
and make them publicly available. Additionally, we present plT5 - a
general-purpose text-to-text model for Polish that can be fine-tuned on various
Natural Language Processing (NLP) tasks with a single training objective.
Unsupervised denoising pre-training is performed efficiently by initializing
the model weights with a multi-lingual T5 (mT5) counterpart. We evaluate the
performance of plT5, mT5, Polish BART (plBART), and Polish GPT-2 (papuGaPT2).
The plT5 scores top on all of these tasks except summarization, where plBART is
best. In general (except for summarization), the larger the model, the better
the results. The encoder-decoder architectures prove to be better than the
decoder-only equivalent.
