# Evaluation of Transfer Learning for Polish with a Text-to-Text Model Source: [Evaluation of Transfer Learning for Polish with a Text-to-Text Model]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:14:46.163Z Authors: Aleksandra Chrabrowa, {\\L}ukasz Dragan, Karol Grzegorczyk, Dariusz Kajtoch, Miko{\\l}aj Koszowski, Robert Mroczkowski, Piotr Rybak Published: 2022-05-18T09:17:14.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()