Fine-Tuning Large Language Models for Kazakh Text Simplification
Сохранить в:
| Опубликовано в:: | Applied Sciences vol. 15, no. 15 (2025), p. 8344-8367 |
|---|---|
| Главный автор: | |
| Другие авторы: | , |
| Опубликовано: |
MDPI AG
|
| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|
| Краткий обзор: | This paper addresses text simplification task for Kazakh, a morphologically rich, low-resource language, by introducing KazSim, an instruction-tuned model built on multilingual large language models (LLMs). First, we develop a heuristic pipeline to identify complex Kazakh sentences, manually validating its performance on 400 examples and comparing it against a purely LLM-based selection method; we then use this pipeline to assemble a parallel corpus of 8709 complex–simple pairs via LLM augmentation. For the simplification task, we benchmark KazSim against standard Seq2Seq systems, domain-adapted Kazakh LLMs, and zero-shot instruction-following models. On an automatically constructed test set, KazSim (Llama-3.3-70B) achieves BLEU 33.50, SARI 56.38, and F1 87.56 with a length ratio of 0.98, outperforming all baselines. We also explore prompt language (English vs. Kazakh) and conduct human evaluation with three native speakers: KazSim scores 4.08 for fluency, 4.09 for meaning preservation, and 4.42 for simplicity—significantly above GPT-4o-mini. Error analysis shows that remaining failures cluster into tone change, tense change, and semantic drift, reflecting Kazakh’s agglutinative morphology and flexible syntax. |
|---|---|
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app15158344 |
| Источник: | Publicly Available Content Database |