Papers by Dawid Motyka

2 papers
Rethinking the Evaluation of Alignment Methods: Insights into Diversity, Generalisation, and Safety (2026.eacl-srw)

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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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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.

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