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    <titleInfo>
        <title>How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?</title>
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        <namePart type="given">Kateryna</namePart>
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            <title>Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)</title>
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            <namePart type="given">Mariana</namePart>
            <namePart type="family">Romanyshyn</namePart>
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    <abstract>Fine-tuned Large Language Models (LLMs) dominate in Ukrainian grammatical error correction (GEC), while API-accessed LLMs remain nearly untested on minimal-edit benchmarks. We evaluate 11 commercial LLMs from four providers and one open-source Ukrainian model on the UNLP 2023 GEC-only benchmark, comparing zero-shot, few-shot, minimal-edits, and LLM-assisted prompt optimization strategies. Our best configuration (Gemini 3.1-Pro) reaches F0.5=69.22, closing over 90% of the gap to fine-tuned SOTA (F0.5=73.14). For zero-shot prompts, only Claude models benefit from Ukrainian instructions. However, the best overall results for all models use Ukrainian minimal-edits prompts, whose language-specific rules require Ukrainian to express precisely. LLM-assisted prompt optimization on top of minimal-edits + few-shot achieves the highest score. Detailed minimal-edits instructions yield the largest gains for punctuation and case errors but cause the model to abandon several low-frequency categories. Delving into error analysis, we identify five recurring overcorrection patterns tied to Ukrainian-specific linguistic phenomena. Code, prompts, and outputs are publicly available.</abstract>
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        <url>https://aclanthology.org/2026.unlp-1.13/</url>
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%0 Conference Proceedings
%T How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?
%A Karpo, Kateryna
%A Chernodub, Artem
%Y Romanyshyn, Mariana
%S Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)
%D 2026
%8 May
%I Association for Computational Linguistics
%C Lviv, Ukraine
%@ 979-8-89176-359-3
%F karpo-chernodub-2026-far
%X Fine-tuned Large Language Models (LLMs) dominate in Ukrainian grammatical error correction (GEC), while API-accessed LLMs remain nearly untested on minimal-edit benchmarks. We evaluate 11 commercial LLMs from four providers and one open-source Ukrainian model on the UNLP 2023 GEC-only benchmark, comparing zero-shot, few-shot, minimal-edits, and LLM-assisted prompt optimization strategies. Our best configuration (Gemini 3.1-Pro) reaches F0.5=69.22, closing over 90% of the gap to fine-tuned SOTA (F0.5=73.14). For zero-shot prompts, only Claude models benefit from Ukrainian instructions. However, the best overall results for all models use Ukrainian minimal-edits prompts, whose language-specific rules require Ukrainian to express precisely. LLM-assisted prompt optimization on top of minimal-edits + few-shot achieves the highest score. Detailed minimal-edits instructions yield the largest gains for punctuation and case errors but cause the model to abandon several low-frequency categories. Delving into error analysis, we identify five recurring overcorrection patterns tied to Ukrainian-specific linguistic phenomena. Code, prompts, and outputs are publicly available.
%U https://aclanthology.org/2026.unlp-1.13/
%P 136-154

Markdown (Informal)

[How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?](https://aclanthology.org/2026.unlp-1.13/) (Karpo & Chernodub, UNLP 2026)

• How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction? (Karpo & Chernodub, UNLP 2026)

ACL

• Kateryna Karpo and Artem Chernodub. 2026. How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?. In Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026), pages 136–154, Lviv, Ukraine. Association for Computational Linguistics.