## 5 Conclusions  
This study verified through experiments the significant impact of the three set scoring indicators \(D_{1}\), \(D_{2}\), and \(D_{3}\) on the generation effect of the large language model. Experiments have shown that as the scores of these three indicators increase, the quality of the answers generated by the large language model improves accordingly. A particularly exciting finding is that as the comprehensive evaluation score \(S\) improves, there is a significant optimization in the generation results. This outcome suggests that the input text quality evaluation criteria designed in this paper are effective for assessing the generation effects of large language models in the field of cybersecurity, and this criterion has the potential to be applied to other vertical fields, such as medicine and law. The key difference in applying it across various fields lies in training the corresponding \(D_{3}\) indicator model for the specialized vocabulary of each field.  
The study of input text quality is of significant importance for improving the generative effects of large language models. While the Transformer architecture can comprehensively extract input information, variations in the way questions are posed in professional, vertical fields can lead to significant differences in the effectiveness and depth of generated results. This research, based on the three indicators proposed that affect generation quality, has constructed an input text quality standard \(S\), which effectively evaluates input quality and thereby supports the application of large language models in fields such as cybersecurity, filling a gap in research on the quality of inputs for large language models. Additionally, this paper primarily focuses on the vertical field of cybersecurity, conducting validation experiments based on a cybersecurity knowledge base. In future research, knowledge from other fields will be gradually established to extend the applicability of this algorithm.  
Future research directions and focuses will include:  
1. Building more structured input models based on the existing foundation.  
Figure 11: Question (\(S=0.51\)).  
Continuously optimizing the details of the indicators so that the \(S\) can more accurately reflect input quality.
* This will be applied in specialized large language models for specific vertical fields.
* Building knowledge in other fields to promote the general applicability of this algorithm.
* Conduct a more detailed analysis of input text research in the field of cybersecurity, including the examination of necessary hardware information and related parameters for specific issues. Additionally, integrate advanced algorithms to enhance the analysis results.  
Conceptualization, J.-K.Y.; Methodology, J.-K.Y.; Software, Y.-F.Y.; Validation, Y.-F.Y.; Formal analysis, Y.-F.Y. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Key R&D Program of China under Grant No. 2024 QY1703 and National Natural Science Foundation of China under Grant No. U1636208.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.