ZNO-Eval: Benchmarking reasoning capabilities of large language models in Ukrainian

TL;DR

This paper introduces ZNO-Eval, a comprehensive benchmark for assessing reasoning abilities of large language models in Ukrainian, based on real exam tasks across multiple subjects, revealing strengths and gaps in current models.

Contribution

The paper presents the first Ukrainian reasoning benchmark derived from standardized exams, enabling detailed evaluation of LLMs across diverse subjects and complexities.

Findings

GPT-4o outperforms others in reasoning and language tasks.

Gemini Pro and GPT-4 Turbo excel in arithmetic problems.

Models perform near maximum in history and geography, but lag in Ukrainian language and math.

Abstract

As the usage of large language models for problems outside of simple text understanding or generation increases, assessing their abilities and limitations becomes crucial. While significant progress has been made in this area over the last few years, most research has focused on benchmarking English, leaving other languages underexplored. This makes evaluating the reasoning and robustness level of language models in Ukrainian particularly challenging. The purpose of this work is to establish a comprehensive benchmark for the reasoning capabilities evaluation of large language models in the Ukrainian language. This paper presents the ZNO-Eval benchmark based on real exam tasks from Ukraine's standardized educational testing system: the External Independent Evaluation and the National Multi-subject Test. With single-answer options, multiple-choice, matching, and open-ended questions from diverse subjects, including Ukrainian language, mathematics, history, and geography, this dataset paves the way toward a thorough analysis of reasoning capabilities across different domains and complexities. Evaluation of several well-known language models, such as GPT-3.5-Turbo, GPT-4o, GPT-4-Turbo, Mistral Large, Claude 3 Opus, and Gemini-1.5 Pro on this benchmark demonstrated the superiority of GPT-4o in both common knowledge reasoning and intricate language tasks. At the same time, Gemini Pro and GPT-4 Turbo excelled in the arithmetic domain, leading in single-answer and open-ended math problems. While all models were close to max performance in text-only common knowledge tasks like history and geography, there still is a gap for Ukrainian language and math, thus highlighting the importance of developing specialized language benchmarks for more accurate assessments of model capabilities and limitations across different languages and contexts.