# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 4 Experiments
### Results  
Tables 5 and 6 present the evaluation results for baseline sequence-to-sequence models and various LLMs on the Kazakh text simplification task. Table 5 presents evaluation results on the Kazakh text simplification dataset. Seq2 Seq baselines fail to generalize, producing negligible BLEU and ROUGE scores, indicating traditional architectures lack sufficient capacity to model simplification under low-resource constraints. Zero-shot LLMs also struggle, with all variants producing overextended outputs (length ratios between 3.17 and 5.38). While Llama-3.3-70B shows moderate gains in BLEU (5.53) and F1 (73.24), the absence of length control limits overall performance. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **BLEU** & **ROUGE-1** & **ROUGE-L** & **Length Ratio** & **Precision** & **Recall** & **F1** \\ \hline kazLLM-Llama-3.1-8B & 17.09 & 38.80\% & 36.96\% & 1.04 & 82.52 & 83.13 & 82.78 \\ kazLLM-Llama-3.1-70B & 16.35 & 41.24\% & 39.59\% & 1.25 & 81.84 & 85.76 & 83.72 \\ Sherkala-Llama-3.1-8B & 17.08 & 40.61\% & 38.63\% & 1.13 & 82.80 & 84.40 & 83.55 \\ \hline Llama-3.2-3B (zero-shot) & 0.002 & 0.67\% & 0.67\% & 5.60 & 57.11 & 59.96 & 58.42 \\ Qwen2-72B (zero-shot) & 0.012 & 3.51\% & 3.19\% & 4.30 & 60.33 & 64.11 & 61.94 \\ Llama-3.3-70B (zero-shot) & 0.045 & 20.71\% & 19.21\% & 3.06 & 69.80 & 74.98 & 72.08 \\ \hline KazSim (Llama-3.2-3B) & 17.82 & 39.89\% & 38.49\% & 1.01 & 83.72 & 83.11 & 83.37 \\ KazSim (Qwen2-72B) & 18.31 & 40.16\% & 38.83\% & 0.97 & 75.20 & 81.14 & 78.03 \\ KazSim (Llama-3.3-70B) & **20.33** & 42.26\% & 40.50\% & 0.99 & **84.76** & **83.87** & **84.27** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results on the semi-manually created test set for Kazakh text simplification. Bolded values indicate the highest performance across all models for each metric. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **BLEU** & **ROUGE-1** & **ROUGE-L** & **Length Ratio** & **Precision** & **Recall** & **F1** \\ \hline Seq2 Seq-small & 0.0038 & 0.25\% & 0.25\% & 0.90 & 66.26 & 65.86 & 66.04 \\ Seq2 Seq-large & 0.007 & 3.81\% & 3.63\% & 0.82 & 67.39 & 65.88 & 66.61 \\ \hline kazLLM-Llama-3.1-8B & 20.72 & 42.09\% & 40.57\% & 1.22 & 82.52 & 84.51 & 83.45 \\ kazLLM-Llama-3.1-70B & 21.52 & 44.57\% & 43.70\% & 1.06 & 81.63 & 86.65 & 84.04 \\ Sherkala-Llama-3.1-8B & 19.59 & 42.35\% & 40.83\% & 1.17 & 82.58 & 85.25 & 83.85 \\ \hline Llama-3.2-3B (zero-shot) & 0.0028 & 0.56\% & 0.57\% & 5.38 & 57.57 & 60.47 & 58.91 \\ Qwen2-72B (zero-shot) & 0.013 & 3.08\% & 2.97\% & 4.27 & 59.71 & 63.39 & 61.28 \\ Llama-3.3-70B (zero-shot) & 0.055 & 23.47\% & 22.47\% & 3.17 & 70.12 & 77.13 & 73.24 \\ \hline KazSim (Llama-3.2-3B) & 25.7 & 47.01\% & 46.02\% & 0.99 & 84.97 & 85.61 & 85.25 \\ KazSim (Qwen2-72B) & 27.8 & 48.56\% & 47.60\% & 0.96 & 75.20 & 81.14 & 78.03 \\ KazSim (Llama-3.3-70B) & **33.5** & 54.21\% & 53.00\% & 0.98 & **87.49** & **87.70** & **87.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results on the Kazakh text simplification dataset. Bolded values indicate the highest performance across all models for each metric. Domain-specific models, including kazLLM and Sherkala, demonstrate stable performance across all metrics and clearly outperform both Seq2 Seq baselines and zero-shot LLMs. BLEU scores range from 19.59 to 21.52, and all three configurations achieve F1 scores above 83, indicating strong surface-level fluency and content preservation. In contrast to zero-shot outputs, length ratios remain close to 1.0, confirming better control over generation length. Among these models, kazLLM-70B achieves the highest BLEU score (21.52) and the highest recall (86.65), with a length ratio of 1.06. Sherkala-8B, while slightly behind in BLEU (19.59), achieves the highest precision (82.58) and a longer average output (length ratio = 1.17).