Andrian Kravchenko


Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here


2025

pdf bib
UAlign: LLM Alignment Benchmark for the Ukrainian Language
Andrian Kravchenko | Yurii Paniv | Nazarii Drushchak
Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)

This paper introduces UAlign, the comprehensive benchmark for evaluating the alignment of Large Language Models (LLMs) in the Ukrainian language. The benchmark consists of two complementary components: a moral judgment dataset with 3,682 scenarios of varying ethical complexities and a dataset with 1,700 ethical situations presenting clear normative distinctions. Each element provides parallel English-Ukrainian text pairs, enabling cross-lingual comparison. Unlike existing resources predominantly developed for high-resource languages, our benchmark addresses the critical need for evaluation resources in Ukrainian. The development process involved machine translation and linguistic validation using Ukrainian language models for grammatical error correction. Our cross-lingual evaluation of six LLMs confirmed the existence of a performance gap between alignment in Ukrainian and English while simultaneously providing valuable insights regarding the overall alignment capabilities of these models. The benchmark has been made publicly available to facilitate further research initiatives and enhance commercial applications.Warning: The datasets introduced in this paper contain sensitive materials related to ethical and moral scenarios that may include offensive, harmful, illegal, or controversial content.