@inproceedings{suppa-etal-2026-sk,
title = "sk-bench: A Native-First Benchmark for Evaluating Large Language Models in {S}lovak",
author = "{\v{S}}uppa, Marek and
Vykopal, Ivan and
Ridzik, Andrej and
Sopkovi{\v{c}}, Kristi{\'a}n and
K{\v{n}}a{\v{z}}ekov{\'a}, Nat{\'a}lia and
Kopcan, Jaroslav and
Blstak, Miroslav and
Ondrejov{\'a}, Vikt{\'o}ria and
Hl{\'a}dek, Daniel and
Gregor, Michal and
Tamajka, Martin and
Simko, Marian",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2639/",
pages = "60008--60055",
ISBN = "979-8-89176-459-0",
abstract = "Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation. We present sk-bench, a native-first Slovak benchmark with 30 datasets (33 scored task variants) across ten skill categories. Eleven resources are introduced or first packaged for generative-LLM evaluation, including IFEval-SK with Slovak-adapted instruction checkers and native Chiby/SKJ1 resources for Slovak grammar and morphology. We evaluate 55 open- and closed-weights models under one harness. The best open model trails proprietary APIs by 12.6 points. Model rankings are similar for native and translated closed-form data ($\rho\geq0.98$), though translation separates the strongest models less well. By contrast, human-authored and LLM-generated QA questions rank models differently ($\rho=0.72$). For Qwen3-14B, continued Slovak pretraining lowers the overall score by 13.9 points. A small instruction set restores three quarters of that loss. Test-time reasoning improves scores by 8.5 to 12.5 points for models of 9B and above. Together, these findings suggest four design lessons for other under-resourced languages: use native data where translation fails, plan instruction repair after language adaptation, enable test-time reasoning before scaling up, and avoid overinvesting in target-language prompts. We release the data and code at https://github.com/slovak-nlp/sk-bench"
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<abstract>Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation. We present sk-bench, a native-first Slovak benchmark with 30 datasets (33 scored task variants) across ten skill categories. Eleven resources are introduced or first packaged for generative-LLM evaluation, including IFEval-SK with Slovak-adapted instruction checkers and native Chiby/SKJ1 resources for Slovak grammar and morphology. We evaluate 55 open- and closed-weights models under one harness. The best open model trails proprietary APIs by 12.6 points. Model rankings are similar for native and translated closed-form data (ρ\geq0.98), though translation separates the strongest models less well. By contrast, human-authored and LLM-generated QA questions rank models differently (ρ=0.72). For Qwen3-14B, continued Slovak pretraining lowers the overall score by 13.9 points. A small instruction set restores three quarters of that loss. Test-time reasoning improves scores by 8.5 to 12.5 points for models of 9B and above. Together, these findings suggest four design lessons for other under-resourced languages: use native data where translation fails, plan instruction repair after language adaptation, enable test-time reasoning before scaling up, and avoid overinvesting in target-language prompts. We release the data and code at https://github.com/slovak-nlp/sk-bench</abstract>
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%0 Conference Proceedings
%T sk-bench: A Native-First Benchmark for Evaluating Large Language Models in Slovak
%A Šuppa, Marek
%A Vykopal, Ivan
%A Ridzik, Andrej
%A Sopkovič, Kristián
%A Kňažeková, Natália
%A Kopcan, Jaroslav
%A Blstak, Miroslav
%A Ondrejová, Viktória
%A Hládek, Daniel
%A Gregor, Michal
%A Tamajka, Martin
%A Simko, Marian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F suppa-etal-2026-sk
%X Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation. We present sk-bench, a native-first Slovak benchmark with 30 datasets (33 scored task variants) across ten skill categories. Eleven resources are introduced or first packaged for generative-LLM evaluation, including IFEval-SK with Slovak-adapted instruction checkers and native Chiby/SKJ1 resources for Slovak grammar and morphology. We evaluate 55 open- and closed-weights models under one harness. The best open model trails proprietary APIs by 12.6 points. Model rankings are similar for native and translated closed-form data (ρ\geq0.98), though translation separates the strongest models less well. By contrast, human-authored and LLM-generated QA questions rank models differently (ρ=0.72). For Qwen3-14B, continued Slovak pretraining lowers the overall score by 13.9 points. A small instruction set restores three quarters of that loss. Test-time reasoning improves scores by 8.5 to 12.5 points for models of 9B and above. Together, these findings suggest four design lessons for other under-resourced languages: use native data where translation fails, plan instruction repair after language adaptation, enable test-time reasoning before scaling up, and avoid overinvesting in target-language prompts. We release the data and code at https://github.com/slovak-nlp/sk-bench
%U https://aclanthology.org/2026.emnlp-main.2639/
%P 60008-60055
Markdown (Informal)
[sk-bench: A Native-First Benchmark for Evaluating Large Language Models in Slovak](https://aclanthology.org/2026.emnlp-main.2639/) (Šuppa et al., EMNLP 2026)
ACL
- Marek Šuppa, Ivan Vykopal, Andrej Ridzik, Kristián Sopkovič, Natália Kňažeková, Jaroslav Kopcan, Miroslav Blstak, Viktória Ondrejová, Daniel Hládek, Michal Gregor, Martin Tamajka, and Marian Simko. 2026. sk-bench: A Native-First Benchmark for Evaluating Large Language Models in Slovak. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60008–60055, Budapest, Hungary. Association for Computational Linguistics.