## 4 Experimental Results and Analyses
### Ablation Experiment  
We further validate the effectiveness of fine-tuning large language models and integrating local knowledge bases through ablation experiments. Fine-tuning aims to adapt large language models to achieve enhanced performance in specific domains, enabling the model to capture domain-specific information more accurately and improve the quality of its responses. Consequently, evaluating the performance of the fine-tuned model is essential. Similarly, integrating a local knowledge base enhances answer accuracy and professionalism by incorporating domain-specific resources, which, in turn, improves system stability and credibility--thus necessitating performance verification.  
In our ablation experiments, we employed a plant protection knowledge evaluation dataset to assess the impact of these two functions. Specifically, we removed the fine-tuning and knowledge base modules individually and measured the resulting changes in ROUGE, BERTScore, and FactCC.  
As shown in Table 3, progressively removing the fine-tuning and knowledge base modules significantly degrades the performance of the plant protection question-answering system. In the complete system, ROUGE-1, ROUGE-2, and ROUGE-L scores are 0.578, 0.321, and 0.377, respectively, and the BERTScore F1 Score is 0.970, indicating strong performance in both generation quality and semantic consistency.  
When the fine-tuning module is removed, ROUGE-1, ROUGE-2, and ROUGE-L decline to 0.487, 0.273, and 0.304, respectively, while the BERTScore metrics drop to 0.960. These changes demonstrate that fine-tuning significantly enhances the model's keyword coverage, language expression accuracy, and semantic relevance. Upon further removal of the knowledge base module, ROUGE-1 falls to 0.394, ROUGE-2 to 0.139, and ROUGE-L to 0.254, while the BERTScore F1 Score decreases to 0.953. The experimental results show that the FactCC F1 scores varied significantly when different techniques were applied to the baseline model. The baseline model, InternLM-20B, achieved a FactCC F1 score of 0.829. By implementing LoRA alone, this metric improved to 0.846, confirming that effective parameter tuning can enhance factual consistency and domain-specific performance. Notably, introducing the external knowledge base module alone to the baseline model increased the score to 0.859. This highlights that while fine-tuning remains crucial, the local knowledge base also plays a vital role in ensuring factual accuracy. This result underscores the substantial contribution of the local knowledge base to improving the professionalism and accuracy of system responses, particularly by providing more comprehensive and credible content.  
Compared to the pre-trained InternLM-20B, the plant protection question-answering system exhibits substantially improved performance, validating the necessity and effectiveness of both fine-tuning and local knowledge base integration. The combination of these enhancements not only boosts model performance in specific domains but also highlights the value of a modular design in domain-specific question-answering systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**ROUGE-1**} & \multirow{2}{*}{**ROUGE-2**} & \multirow{2}{*}{**ROUGE-L**} & \multicolumn{2}{c}{**BERTScore**} & \multirow{2}{*}{**FactCC**} \\ \cline{3-4} \cline{6-6}  & & & & **F1 Score** & & **F1 Score** \\ \hline InternLM-20B & 0.324 & 0.121 & 0.200 & 0.936 & 0.829 \\ InternLM-20B + RAG & 0.487 & 0.273 & 0.304 & 0.960 & 0.859 \\ InternLM-20B + LoRA & 0.394 & 0.139 & 0.254 & 0.953 & 0.846 \\ Plant Protection QA System & **0.578** & **0.321** & **0.377** & **0.970** & **0.897** \\ \hline \hline \end{tabular} Note: the best results are highlighted in bold.  
\end{table}
Table 3: Ablation analysis of LoRA and RAG on the performance of the plant protection QA system.