Papers by Aleksandra Krasnodębska
Multilingual Refusal Alignment for Safer Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used globally, but their safety and alignment can vary unpredictably between languages. |
| Approach: | They propose a multilingual refusal alignment dataset to investigate whether alignment transfers cross-lingually and how language consistency is preserved during training. |
| Outcome: | The proposed model can be trained on multilingual datasets without affecting general performance. |
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. |