Papers by Karolina Seweryn
Rethinking the Evaluation of Alignment Methods: Insights into Diversity, Generalisation, and Safety (2026.eacl-srw)
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Denis Janiak, Julia Moska, Dawid Motyka, Karolina Seweryn, Paweł Walkowiak, Bartosz Żuk, Arkadiusz Janz
| Challenge: | Existing studies focus on individual techniques or specific dimensions, lacking a holistic assessment of the inherent trade-offs. |
| Approach: | They propose a framework that compares LLM alignment methods across five axes . they use a validated LLM-as-judge prompt to compare the results . |
| Outcome: | The proposed framework compares LLM alignment methods across factuality, safety, conciseness, proactivity, diversity and safety axes . it provides insights into trade-offs of common alignment methods, guiding the development of more balanced and reliable LLMs. |
PLLuM-Align: Polish Preference Dataset for Large Language Model Alignment (2025.emnlp-main)
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Karolina Seweryn, Anna Kołos, Agnieszka Karlińska, Katarzyna Lorenc, Katarzyna Dziewulska, Maciej Chrabaszcz, Aleksandra Krasnodebska, Paula Betscher, Zofia Cieślińska, Katarzyna Kowol, Julia Moska, Dawid Motyka, Paweł Walkowiak, Bartosz Żuk, Arkadiusz Janz
| Challenge: | Large language models generate preferred responses while avoiding harmful or inappropriate outputs, despite their ability to generate cross-language transferability. |
| Approach: | They introduce the first Polish preference dataset PLLuM-Align, created entirely through human annotation to reflect Polish language and cultural nuances. |
| Outcome: | The proposed dataset lays the groundwork for more aligned Polish LLMs and contributes to the broader goal of multilingual alignment in underrepresented languages. |
Safety of Large Language Models Beyond English: A Systematic Literature Review of Risks, Biases, and Safeguards (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have a growing number of applications that generate harmful, biased, or unsafe content. |
| Approach: | They synthesize findings from recent studies that evaluate their robustness across languages . they highlight gaps in multilingual safety research and recommend future work . |
| Outcome: | The systematic review examines the multilingual safety of large language models in English . it identifies challenges such as dataset availability and evaluation biases . |
Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework (2026.eacl-srw)
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Grzegorz Statkiewicz, Alicja Dobrzeniecka, Karolina Seweryn, Aleksandra Krasnodębska, Karolina Piosek, Katarzyna Bogusz, Sebastian Cygert, Wojciech Kusa
| Challenge: | Currently, most vision-language models are trained on English-centric data, limiting their usability for non-English-speaking users. |
| Approach: | They reproduce and adapt LLaVA-Next methodology to create Polish VLMs . they use a fully automated pipeline for translating and filtering existing multimodal datasets based on Polish data for OCR and culturally specific tasks. |
| Outcome: | The proposed model improves on a Polish-adapted model and shows higher quality captions in generative evaluations. |