## 3 Results
### Expanded Lexicon
The training loss gradually reduced from 1.4 to around 0.8 through iterations. This indicates significant error reduction and effective fitting. Although there were fluctuations in loss during early training, they diminished over time, demonstrating the model's strong convergence and reduced error. These fluctuations are likely due to batch-to-batch variability or the model's adjustment to more complex patterns. The dashed trend line illustrates this decline, confirming the model's effective and stable fine tuning. Additionally, the consistent decrease in training loss and stability in later stages indicates successful model optimization and parameter adjustments. These outcomes suggest that hyperparameters, like the learning rate and batch size, were effectively set for optimal learning. Future research could build on these findings by refining the model's structure and parameters to explore further loss reduction and generalization improvements. In this study, we chose not to explicitly partition the dataset into training and validation sets. This decision was informed by the specific nature of our task, which emphasizes fine tuning the language model rather than performing a traditional supervised learning task. Given that the pre-training and fine tuning processes are designed to enhance the model's performance on unlabeled data, the use of a validation set was considered unnec  
Figure 7: The trend of training loss over training steps during the domain-specific fine tuning process. essary at this stage. However, to mitigate any potential shortcomings of this approach, we introduced a gas pipeline network case matching task as an auxiliary method to assess the model's effectiveness. This task involves inputting the textual description of an incident case and generating the most similar historical event as an output. Table 6 presents an example of a historical case matching result, where the analysis indicates that these similar cases share commonalities with the input case in terms of geographic location, incident outcomes, causes, and other relevant factors. According to the National Gas Accident Analysis Report, the majority of gas pipeline network accidents are primarily caused by third-party construction, pipeline corrosion, and other related factors, collectively accounting for more than half of the reported incidents [50]. Considering that the primary causes of gas pipeline emergencies stem from external impacts, human activities, and environmental factors, we selected representative causes from these categories and identified the ten most similar terms using cosine similarity. Table 7 shows that terms closely related to \"third-party construction\" mainly involve construction-related aspects like \"construction personnel\", \"engineering construction\", and \"construction site\", all demonstrating high similarity scores. Notably, the highest similarity score was 0.794868, demonstrating the model's capability to precisely identify activities and entities associated with \"third-party construction\". Moreover, terms linked to \"pipeline corrosion\" cover specialized areas, such as various corrosion types (\"metal corrosion\", \"soil corrosion\"), and specific concepts, like \" corrosion depth\" and \"corrosion prevention\". This suggests that the model not only grasps the fundamental concept of \"corrosion\" but also identifies how it manifests across various materials and environmental conditions, with similarity scores ranging from 0.790442 to 0.822075. This indicates the model's proficiency in recognizing specialized technical terms. Furthermore, the term \"non-compliant operations\" and related terms like \"violative operations\" (similarity 0.971644) show high similarity, emphasizing the model's sensitivity to norm violations. This highlights the model's enhanced sensitivity to breaches of conduct norms. Additionally, terms such as \"operational error\" and \"mistaken operation\" further demonstrate the model's ability to detect inappropriate actions and their potential consequences.