## 6 Conclusions and Future Work
### Future Work  
During the course of this study, we also recognized the limitations of GPT-4 in information extraction tasks. Future research can focus on the following aspects:  
1. The current work does not fully address the challenges faced by GPT-4 in handling hierarchical dependencies and entity disambiguation. At this stage, the GPT-4 model primarily interprets multi-level requirement structures and polysemous entities. Future research could explore integrating GPT-4 with dependency syntax analysis [58] for more precise calibration and optimization of the design of prompts. Additionally, the application of Chain of Thought (CoT) [59] methods in comparison with existing approaches could facilitate a deeper analysis of multi-level requirement structures and clarify the specific roles of entities within these requirements, thereby enhancing the interpretability and accuracy of data extraction using GPT-4.
2. The prompts designed in this study utilize few-shot learning for information extraction; however, varying prompting strategies, such as zero-shot, one-shot [60], and CoT, may influence the model's extraction accuracy, which remains an area for further research. Additionally, as a general-purpose language model, GPT-4 may notfully satisfy the stringent accuracy requirements of specific domains when processing domain-specific data. Therefore, future research could investigate the potential of domain-specific fine-tuning of GPT-4 to enhance its accuracy within those specialized fields.
* Although this study designed an automatic format validation mechanism to correct issues related to hallucinations and inconsistencies in structured data generated using GPT-4, the model is still influenced by the training data [61] during the semantic understanding and content generation processes, leading to errors in some cases. To address this issue, future research could explore more refined post-processing mechanisms to verify the accuracy of generated content and reduce the occurrence of hallucinations.
* Due to the complexity of node relationships in Chinese requirement texts, the current system has not yet implemented uniform relationship edges connecting all nodes, which remains a problem to be further investigated. Additionally, the research in this paper mainly focuses on requirement document representation, and there is still significant room for exploration regarding the implicit relationships between different requirements and requirement mining. Future work could delve deeper into these aspects.  
L.W.: Conceptualization, Resources, Writing\(-\)review and editing: M.-C.W.: Conceptualization, Methodology, Software, Writing\(-\)original draft; Y.-R.Z.: Methodology, Investigation; Visualization, Validation; J.M.: Writing\(-\)Review and Editing; H.-Y.S.: Writing\(-\)Review and Editing; Z.-X.C.: Methodology, Data curation, Writing\(-\)Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Projects of China (No. 2024 YFB3309900).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.