## 4 Experimental Results and Analyses
### Comparison with Other Models  
This paper compares the performance of several models in plant protection question-answering tasks, including InternLM-20B, InternLM-20B-LoRA, BaiChuan-13B, ChatGPT, and our dedicated plant protection question-answering system. As shown in Table 2, based on ROUGE, BERTScore, and FactCC metrics, our system exhibits significant advantages. Specifically, the ROUGE-1 and ROUGE-L scores of 0.578 and 0.377, respectively, indicate that the generated text closely matches the reference answers in terms of keyword coverage and overall expression. Moreover, a ROUGE-2 score of 0.321 confirms its superior phrase-level accuracy, demonstrating a more precise handling of complex sentences in the domain.  
BERTScore evaluation indicates that our plant protection question-answering system achieved F1 scores of 0.970, demonstrating superior semantic understanding and generation accuracy. In contrast, the InternLM-20B-LoRA model--fine-tuned with LoRA--recorded F1 scores of 0.953, underscoring the effectiveness of fine-tuning in enhancing domain adaptability. General-purpose models such as ChatGPT and BaiChuan-13B performed relatively poorly; although ChatGPT shows competitiveness in semantic evaluation, its accuracy and domain-specific professionalism remain inferior to those of models optimized for plant protection.  
InternLM-20B achieves a FactCC score of 0.829, while InternLM-20B-LoRA--fine-tuned with LoRA--improves this score to 0.846, indicating that meticulous fine-tuning plays a positive role in enhancing domain adaptability and factual consistency. BaiChuan-13B scores 0.832, similar to InternLM-20B; meanwhile, ChatGPT obtains a FactCC score of 0.857. Although ChatGPT performs well in semantic understanding, its professionalism and domain specificity are slightly inferior to those of the specially optimized models. Notably, our plant protection question-answering system achieves the highest FactCC score of 0.897, demonstrating a significant advantage in ensuring the generated responses align with the reference facts.  
Overall, the comprehensive analysis demonstrates that integrating fine-tuning technology with an external knowledge base substantially enhances domain-specific performance. Our plant protection question-answering system excels in both accuracy and professionalism, validating the effectiveness of this approach. Moreover, LoRA shows significant potential in improving the domain adaptability of large language models, offering valuable insights for constructing domain-specific systems. In contrast, general-purpose models exhibit notable limitations in specialized tasks and require further fine-tuning or knowledge enhancement.  
These experimental results confirm that meticulously fine-tuning a large language model on a high-quality plant protection dataset--combined with an external professional knowledge base--can markedly optimize performance, while also providing a novel technical perspective and development direction for 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-LoRA & 0.394 & 0.139 & 0.254 & 0.953 & 0.846 \\ BaiChuan-13B & 0.317 & 0.114 & 0.189 & 0.938 & 0.832 \\ ChatGPT & 0.364 & 0.159 & 0.239 & 0.945 & 0.857 \\ 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 2: Experimental results of the plant protection QA system and other reference models on the plant protection dataset.