---
title: "Śmigiel Dataset — Strebeyko 2026"
description: "Research on detecting machine-generated text (MGT) has focused mainly on English, even though current LLMs write convincingly in many other languages…"
authors: ["Jakub Strebeyko", "Alina Wróblewska", "Piotr Przybyła"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.828/"
doi: "10.63317/3p7ghe9pfm8v"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/strebeyko-2026-smigiel-dataset-laying-foundations-investigating/"
---
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# Śmigiel Dataset: Laying Foundations for Investigating Machine-Generated Text Detection in Polish

**Authors:** Jakub Strebeyko, Alina Wróblewska, Piotr Przybyła · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.828/) · [PDF](https://aclanthology.org/2026.lrec-1.828.pdf) · [DOI 10.63317/3p7ghe9pfm8v](https://doi.org/10.63317/3p7ghe9pfm8v) · [OpenAlex](https://openalex.org/W7160160208)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · 1 in our library, of the 140673 papers whose bibliographies we read

## Summary
Research on detecting machine-generated text (MGT) has focused mainly on English, even though current LLMs write convincingly in many other languages, including Polish. Before this work, Polish had only one preliminary study, limited to e-mails. The authors built Śmigiel, an openly released resource for training and benchmarking detectors in Polish. They gathered recent, permissively licensed human-written passages from six domains. Sentence-based prefixes taken from these passages were used to prompt eight LLMs to continue each text in its original style. The result is a large raw corpus of generated texts and a cleaned version balanced by source and domain. Baseline experiments with text classifiers show that detection difficulty depends on three factors: the domain, the generating model, and whether similar data appeared in training. General-purpose classifiers transferred well to LLMs they had not seen but performed worse on genres missing from the training data. Śmigiel is the first multi-domain, multi-model MGT benchmark for Polish and was already used in a PolEval 2025 shared task.

## Key points
- The raw corpus contains 462 360 Polish fragments generated by eight LLMs of different sizes. The balanced, cleaned dataset has 64 538 human-written and machine-generated fragments.
- Human-written sources include PLSC, open-coursebooks-pl, Wikiźródła, PolEmo, Allegro Reviews, Filmweb, Filmweb+, TwitterEmo, BAN-PL, Polish Wikipedia, Polish Wikinews and ParlaMint.
- Four domains (literature, reviews, social, wikipedia) appear in both training and test data. The news and parlamint domains are held out for testing only, to measure cross-genre generalisation.
- Prefixes consist of whole sentences segmented with the LAMBO tokeniser. Their minimum length depends on genre, ranging from 26 characters for social media to 50 characters for encyclopedic entries.
- The data is available on Zenodo (doi:10.5281/zenodo.18919631) and served as the basis for Task 1 of PolEval 2025.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{strebeyko2026smigiel,
  title = {{Śmigiel Dataset: Laying Foundations for Investigating Machine-Generated Text Detection in Polish}},
  author = {Strebeyko, Jakub and Wróblewska, Alina and Przybyła, Piotr},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3p7ghe9pfm8v},
  url = {https://doi.org/10.63317/3p7ghe9pfm8v}
}
```

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