## 4 Experimental Results
### Result Analysis
These results are recorded in Figure 6 below. Detailed data are shown in Figure 6. The results show that as the scores of \(D_{1}\), \(D_{2}\), and \(D_{3}\) increase, the \(S_{V\text{-}L}\) score exhibits a growing trend. This finding indicates that as the scores of these variables improve, the quality of the generated answers correspondingly enhances. Based on the aforementioned experimental framework, tests were conducted to examine the impact of the evaluation score \(S\) from low to high on the effectiveness of generative AI answers by Transformer large models. The experimental results indicate that as the \(S\) score increases, the answers generated by the large model become progressively more detailed and professional. This finding confirms the effectiveness of the evaluation score \(S\) in guiding and predicting the output quality of large language models. Questions with higher \(S\) scores, due to optimizations in cue words, structuring, and professional content, enable the model to extract and process information more effectively, thereby generating more precise and in-depth answers. Therefore, these experimental results not only validate the VFS (Vertical Field Score) input evaluation method but also provide valuable insights into how to utilize generative AI large models effectively. Within this research framework, a series of continuous experiments were conducted on a total of 326 questions. During the experiment, the VFS of each input question was systematically improved, and the corresponding V-L (Vertical Length) scores were recorded. Each score is the average of three independent experiments. To more intuitively demonstrate the correlation between the quality of input questions and the quality of generated content, this study randomly selected 10 samples from these questions and subsequently plotted a graph illustrating the relationship between input quality and generation quality. As shown in Figure 7 for the same question, as the VFS increases, there is a significant improvement in the quality of the generated results. This indicates that, although there are certain limitations to improving the input quality for specific questions, the experimental data show a clear trend: a positive correlation exists between higher VFS scores and better quality of generated results. Notably, when the VFS score begins to rise from a lower level, the improvement in generated quality (i.e., V-L score) is relatively rapid. However, as the score further increases, the rate of improvement in generated quality gradually diminishes, indicating a decreasing marginal effect of score improvement on the quality of generation. As shown in Figures 8-10, with the gradual increase in the Vertical Field Score (VFS), it is observed that the quality and length of ChatGPT's generated responses for the same question exhibit a corresponding upward trend. In Figure 11, the generated response is displayed when the input question is modified to \(S\) = 0.51. Compared to Figures 8-10, the input problem in Figure 11 is more complete, featuring clearer descriptions of the details. Each sentence includes a logical connective to articulate the relationships between different sentences. Additionally, there is an enhanced use of specialized vocabulary. It is evident that, compared to Figures 8-10, ChatGPT's response in Figure 11 shows a significant improvement in both length and professionalism. When adjusting the input question, the first step is to eliminate colloquial expressions and provide a clearer description of the question. Next, the logical relationships between sentences are enhanced by adding logical connectors to each sentence, indicating the connections between different sentences. Finally, the actual problem is described in a more specialized manner by replacing general vocabulary with more professional terms whenever possible.