# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 4 Experiments
### Results
kazLLM-8B falls between the two, with balanced precision and recall (82.52/84.51) and a BLEU score of 20.72, showing that smaller-scale models can still generalize well when exposed to sufficient domain-specific data. KazSim models outperform all baselines. KazSim (Llama-3.3-70B) achieves the best overall results, with a BLEU of 33.5, F1 of 87.56, and a near-optimal length ratio of 0.98. Other KazSim variants (Qwen2-72B and Llama-3.2-3B) also perform well, confirming that targeted finetuning on task-specific Kazakh data is critical for achieving high-quality simplification. Table 6 reports results on the semi-manually created test set for Kazakh text simplification. Compared to the generated test, performance patterns remain consistent, but scores are generally lower across all metrics. This drop suggests that the manually curated references are more diverse and structurally dissimilar from the model outputs, increasing the difficulty of achieving high lexical overlap. Zero-shot models again show weak performance, with BLEU scores below 5 and F1 scores ranging from 58.42 to 72.08. Length ratios remain substantially inflated (3.06-5.60), indicating persistent overgeneration. Despite minor gains in F1, these models continue to underperform across all metrics, confirming the limitations of zero-shot simplification in low-resource settings. Domain-specific models, kazLLM and Sherkala, maintain relatively good performance. BLEU scores fall between 16.35 and 17.09, and F1 remains above 82 for all configurations. Length ratios range from 1.04 to 1.25, consistent with more controlled generation behavior. KazSim again outperforms all other approaches. KazSim (Llama-3.3-70B) achieves the highest BLEU (20.33) and F1 (84.27), with a near-optimal length ratio of 0.99. Other KazSim variants (Llama-3.2-3B and Qwen2-72B) also perform strongly, confirming that the benefits of fine-tuning extend to harder test cases with more diverse simplification references. In comparison to the automatic test split, all models show slightly reduced BLEU and ROUGE scores on the manual set. This suggests that the manual references contain more lexical and syntactic variation, reducing surface-level overlap. However, models like KazSim that are trained on task-aligned supervision still generalize well, with minimal drop in F1 and consistent length control. Overall, the results reinforce the robustness of KazSim across evaluation settings and confirm that simplification in low-resource languages requires explicit adaptation not only to the language but also to the task. Figure 4 presents SARI scores for all models evaluated on both the automatic and semi-manual test sets. SARI is used as the primary metric for measuring the simplification quality, as it captures the balance between content preservation, the deletion of unnecessary information, and the appropriate addition of simplified expressions. Seq2 Seq baselines perform the worst, with SARI scores of 33.56 and 33.60. These results reflect the inability of standard encoder-decoder models to generalize under low-resource scenarios. Zero-shot models demonstrate slight performance improvements, with scores ranging from 33.92 (Llama-3.2-3B) to 40.02 (Llama-3.3-70B). Notably, performance on the semi-manual test set remains stable or slightly improves across all zero-shot models. This suggests that zero-shot models are not particularly sensitive to test set construction and may rely on generic rewriting patterns that generalize equally across both test types. Domain-specific models, including kazLLM and Sherkala, achieve higher SARI scores in the 42.86-45.80 range, and show relatively small gaps between the two test sets. This indicates better stability and improved simplification quality when models are pretrained on Kazakh data. KazSim models achieve the highest scores overall. KazSim (Llama-3.3-70B) reaches 56.38 on the automatic test set and 48.42 on the semi-manual set. Other KazSim variants (Qwen2 and Llama-3.2-3B) also outperform all baselines and zero-shot models.