## 3 Results
### Model Performance and Evaluation
If the season of disease occurrence is mentioned in the abstract data, then the answer should be based on the original text's description. If it is not mentioned, then the reply was \"no\". Question 4 is a type of Q&A task that is prone to confusion in answers, as the large language model can easily mistake the year of the fruit tree disease occurrence as the correct answer. Therefore, during the experiment, targeted optimizations were made to the prompts in this regard. For example, the prompt \"what is the time of disease\" was adjusted to \"what is the season of disease\". After optimization, the qwen model and the DeepSeek model showed the best performance, with the qwen model having an accuracy rate and precision rate of 86.00% and 63.64%, respectively, and the DeepSeek model having an accuracy rate and precision rate of 85.00% and 60.00%, respectively. * Question 5: Complex Knowledge Q&A Task  
Question 5 is a complex knowledge Q&A task of higher difficulty, requiring the large language model to answer, using only the abstract text, which parts of the fruit tree are affected by fruit diseases. Usually, the abstract data rarely mention the specific position where the fruit tree disease occurs, so the large language model needs to have a certain level of understanding and reasoning ability to answer accurately, and hence, its accuracy rate is relatively low. After optimizing the prompts, there was a significant improvement in accuracy. In the optimization process of the prompt question, appropriate restrictions were imposed on the site of onset, which was required to be above the fruit tree. If the abstract data do not mention the affected part, then the reply was \"no\". The DeepSeek model still performed the best in the Q&A performance of this question, with an accuracy rate and precision rate of 64.00% and 100.00%, respectively. From the experimental results, it can be seen that the overall performance of large language models on reasoning-based Q&A tasks is poor. * Question 6: Simple Knowledge Q&A Task  
Question 6 is a simple knowledge Q&A task that required the large language model to respond with information about the region where fruit tree diseases occur. The large language model is asked to answer the question with the original abstract text only, and if the region where the disease occurs is not mentioned in the abstract data, then the reply was \"no\". Overall, the five large language models performed well, with the DeepSeek model and the Kimi model showing the best performance in the experiment. The DeepSeek model had an accuracy rate and precision rate of 93.00% and 100.00%, respectively, while the Kimi model had an accuracy rate and precision rate of 95.00% and 96.30%, respectively. * Question 7: Complex Knowledge Q&A Task  
Question 7 requires the large language model to answer the causes of fruit tree diseases, the question belongs to the complex knowledge Q&A task. Usually, the large language model has difficulty finding the answer directly and quickly in the provided abstract data. It often requires understanding and learning the abstract data firstly, and then obtaining the final answer. During this process, in addition to searching for answers in the abstract data, the large language model often conducts online searches to learn related knowledge, which leads to knowledge redundancy or unexpected answers. Therefore, the prompts were specifically optimized to clearly require the large language model to only search and answer within the provided abstract data information, without using the internet to search for additional knowledge to assist in answering. After the prompt optimization, the accuracy of the large language model's answers has significantly improved, with the Kimi model and the DeepSeek model still performing the best. The DeepSeek model has an accuracy rate and precision rate of 56.00% and 89.47%, respectively, while the Kimi model has an accuracy rate and precision rate of 65.00% and 76.09%, respectively.