Improving Named Entity Recognition for Low-Resource Languages Using Large Language Models: A U krainian Case Study (2025)

Abstract: Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), yet achieving high performance for low-resource languages remains challenging due to limited annotated data and linguistic complexity. Ukrainian exemplifies these issues with its rich morphology and scarce NLP resources. Recent advances in Large Language Models (LLMs) demonstrate their ability to generalize across diverse languages and domains, offering promising solutions without extensive annotations. This research explores adapting state-of-the-art LLMs to Ukrainian through prompt engineering, including chain-of-thought (CoT) strategies, and model refinement via Supervised Fine-Tuning (SFT). Our best model achieves 0.89 F1 on the NER-UK 2.0 benchmark, matching the performance of advanced encoder-only baselines. These findings highlight practical pathways for improving NER in low-resource contexts, promoting more accessible and scalable language technologies.

## introduction and motivation accurate identification of named entities underpins a wide range of nlp applications, including information extraction, question answering, and data anonymization, particularly in privacy-sensitive domains such as healthcare, legal document processing, and finance @xcite . however, developing robust ner systems for low-resource languages, such as ukrainian, remains challenging due to the scarcity of annotated datasets and the complexity of linguistic features @xcite . traditional ner approaches, including rulebased methods and early deep learning models, rely on large annotated corpora, which are difficult to obtain for low-resource languages @xcite ). ukrainian's rich morphology and free word order further complicate direct adaptation from resource-rich languages @xcite , leaving a significant performance gap. recent advances in llms offer promising solutions for low-resource ner through zero-shot and few-shot learning, leveraging large-scale pretraining to operate with minimal task-specific data @xcite . techniques such as cot prompting @xcite and sft @xcite further enhance adaptability to linguistic complexity. in this study, we also evaluate state-of-the-art encoder-only models as competitive baselines to assess whether llm-based approaches offer measurable gains. our goal is to address data scarcity in ukrainian ner and contribute to bridging the performance gap between low-and high-resource languages @xcite . the remainder of this paper is structured as follows. section 2 reviews related literature. section 3 defines research gaps and study objectives. section 4 describes the dataset. section 5 outlines the methodology, including model selection, experimental setup, and evaluation. section 6 presents and analyzes the results. section 7 summarizes findings and suggests future directions. section 8 discusses limitations, and covers ethical considerations. ## research gaps and objectives despite progress, ukrainian ner faces key challenges: limited high-quality annotated data, underexplored use of llms, and heavy reliance on proprietary models, which restricts transparency. in addition, the absence of standardized benchmarks hinders consistent evaluation and comparison. to address these gaps, this study pursues the following objectives: ## dataset overview given the limitations of existing resources, we select ner-uk 2.0 @xcite as the primary benchmark for this study. it is the largest public manually annotated ukrainian ner corpus, comprising 560 texts and 21,993 entities across 13 categories. the dataset includes diverse genres-such as news, social media, legal documents, and procurement contracts, and follows the widely adopted inside-outside-beginning labeling scheme. ner-uk 2.0 offers comprehensive entity coverage but has limitations like domain bias, class imbalance (e.g., frequent pers and org vs. rare doc and time), and subjective annotation challenges (e.g. misc). despite these, it remains invaluable for ukrainian ner research. ## conclusion and future work llms demonstrate strong performance for ukrainian ner under minimal supervision, particularly in few-shot settings. however, this comes at the cost of increased computational demands and system complexity. in contrast, generalist models like gliner, while less accurate, offer a more efficient and accessible alternative. supervised fine-tuning of llms yields results comparable to encoder-only baselines but provides limited improvement on low-resource entity types and requires significantly more resources.