УДК 004.8

Д.Є. ЖАРКОВ
Інститут кібернетики ім. В.М. Глушкова НАН України, Київ, Україна,
 dima.zharkov@gmail.com

В.Г. ТУЛЬЧИНСЬКИЙ
Інститут кібернетики ім. В.М. Глушкова НАН України, Київ, Україна,
 dep145@gmail.com

МЕТОД ПІДВИЩЕННЯ ЯКОСТІ БАГАТОКЛАСОВОЇ
КЛАСИФІКАЦІЇ ЗА ДОПОМОГОЮ LLM

СПИСОК ЛІТЕРАТУРИ

1. Miller G.A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review. 1956. Vol. 63, Iss. 2. P. 81–97. https://doi.org/10.1037%2Fh0043158.
2. Saaty T.L. Relative measurement and its generalization in decision making: Why pairwise comparisons are central in mathematics for the measurement of intangible factors — the analytic hierarchy/network process. Review of the Royal Academy of Exact, Physical and Natural Sciences. Series A: Mathematics (RACSAM). 2008. Vol. 102, Iss. 2. P. 251–318. https://doi.org/10.1007%2Fbf03191825.
3. Prompt engineering guide. URL: https://www.promptingguide.ai/ (дата звернення 12.02.2025).
4. Lu Z., Tian J., Wei W., et al. Mitigating boundary ambiguity and inherent bias for text classification in the era of large language models. arXiv:2406.07001v1 [cs.CL] 11 Jun 2024. https://doi.org/10.48550/arXiv.2406.07001.
5. Al Faraby S., Romadhony A., Adiwijaya. Analysis of LLMs for educational question classification and generation. Computers and Education: Artificial Intelligence. 2024. Vol. 7. Article number 100298. https://doi.org/10.1016/j.caeai.2024.100298.
6. Al Faraby S., Adiwijaya, Romadhony A. Educational question classification with pre-trained language models. Proc. 2022 Seventh International Conference on Informatics and Computing (ICIC) (08-09 December 2022, Denpasar, Bali, Indonesia). Denpasar, 2022. P. 1–6. https://doi.org/10.1109/ICIC56845.2022.10006957.
7. Kostina A., Dikaiakos M.D., Stefanidis D., Pallis G. Large language models for text classification: Case study and comprehensive review. arXiv:2501.08457v1 [cs.CL] 14 Jan 2025. https://doi.org/10.48550/arXiv.2501.08457.
8. Wysocki W., Ochodek M. Leveraging LLM-based data augmentation for automatic classification of recurring tasks in software development projects. Journal of Systems and Software. 2025. Vol. 231. Article number 112641. https://doi.org/10.1016/j.jss.2025.112641.
9. Kokkodis M., Demsyn-Jones R., Raghavan V. Beyond the hype: Embeddings vs. prompting for multiclass classification tasks. arXiv:2504.04277v3 [cs.LG] 11 Nov 2025. https://doi.org/10.48550/arXiv.2504.04277.
10. Qin Z., Jaderman R., Hui K., et al. Large language models are effective text rankers with pairwise ranking prompting. arXiv:2306.17563v2 [cs.IR] 28 Mar 2024. https://doi.org/10.48550/arXiv.2306.17563.
11. Ads-Board dataset description. URL: https://github.com/dimazharkov/datasets/tree/main/ ads-board (дата звернення 12.02.2026).
12. SentenceTransformers documentation. URL: https://sbert.net/ (дата звернення 12.02.2026).
13. Ukrainian paraphrase multilingual MPNet base. URL: https://huggingface.co/lang-uk/ ukr-paraphrase-multilingual-mpnet-base (дата звернення 12.02.2026).
14. LLM-driven text classification experiments. URL: https://github.com/dimazharkov/llm-classification (дата звернення 12.02.2026).