The PLLuM Instruction Corpus
Computation and Language
2025-11-24 v1 Artificial Intelligence
Abstract
This paper describes the instruction dataset used to fine-tune a set of transformer-based large language models (LLMs) developed in the PLLuM (Polish Large Language Model) project. We present a functional typology of the organic, converted, and synthetic instructions used in PLLuM and share some observations about the implications of using human-authored versus synthetic instruction datasets in the linguistic adaptation of base LLMs. Additionally, we release the first representative subset of the PLLuM instruction corpus (PLLuMIC), which we believe to be useful in guiding and planning the development of similar datasets for other LLMs.
Cite
@article{arxiv.2511.17161,
title = {The PLLuM Instruction Corpus},
author = {Piotr Pęzik and Filip Żarnecki and Konrad Kaczyński and Anna Cichosz and Zuzanna Deckert and Monika Garnys and Izabela Grabarczyk and Wojciech Janowski and Sylwia Karasińska and Aleksandra Kujawiak and Piotr Misztela and Maria Szymańska and Karolina Walkusz and Igor Siek and Maciej Chrabąszcz and Anna Kołos and Agnieszka Karlińska and Karolina Seweryn and Aleksandra Krasnodębska and Paula Betscher and Zofia Cieślińska and Katarzyna Kowol and Artur Wilczek and Maciej Trzciński and Katarzyna Dziewulska and Roman Roszko and Tomasz Bernaś and Jurgita Vaičenonienė and Danuta Roszko and Paweł Levchuk and Paweł Kowalski and Irena Prawdzic-Jankowska and Marek Kozłowski and Sławomir Dadas and Rafał Poświata and Alina Wróblewska and Katarzyna Krasnowska-Kieraś and Maciej Ogrodniczuk and Michał Rudolf and Piotr Rybak and Karolina Saputa and Joanna Wołoszyn and Marcin Oleksy and Bartłomiej Koptyra and Teddy Ferdinan and Stanisław Woźniak and Maciej Piasecki and Paweł Walkowiak and Konrad Wojtasik and Arkadiusz Janz and Przemysław Kazienko and Julia Moska and Jan Kocoń},
journal= {arXiv preprint arXiv:2511.17161},
year = {2025}
}