## 5 Experiment Results and Analysis
### Empirical Exploration (Answer to RQ5)
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Model Name**} & \multicolumn{5}{c}{**Features**} \\ \cline{3-6}  & & **Case** & **Language** & **Masking** & **Fine-Tuning** \\  & & **Sensitivity** & **Language** & **Strategy** & **Task** \\ \hline LUC & Bert-large-uncased & uncased & English & Standard & - \\ LUC-W & Bert-large-uncased-whole-word-masking & uncased & English & Whole word & - \\ LUC-W-F & Bert-large-uncased-WWM-finetuned-squad & uncased & English & Whole word & SQuAD \\ LC & Bert-large-cased & & cased & English & Standard & - \\ LC-W & Bert-large-cased-whole-word-masking & cased & English & Whole word & - \\ LC-W-F & Bert-large-cased-WWM-finetuned-squad & cased & English & Whole word & SQuAD \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Large model experiments. Figure 10: F1-Score and standard deviation of historical BERT checkpoints on AWARE dataset. ference with fewer parameters. When processing social media or user-generated content, these models better handle mixed-case scenarios more efficiently, learning more contextual information, which enhances their robustness in dealing with varied input forms. Models fine-tuned on the MRPC and SQuAD datasets (e.g., BC-F, LUC/LC-W-F) exhibit slightly lower performance and robustness compared to the original models (e.g., BC, LUC, and LC). This difference is primarily due to the characteristics of the FT datasets. The MRPC dataset focuses on semantic similarity between sentence pairs, while the SQuAD dataset is designed for question-answering tasks. These specific task targets may limit the models' ability to generalize to other tasks. Additionally, the data distribution during FT may differ from the original training data, potentially reducing the models' stability and accuracy when applied to new tasks. As a result, although fine-tuned models perform better on specific tasks, their performance may decline in the generalization of requirements elicitation and evolution. The base models that are multilingual (BC/BUC-M) show the poorest performance and stability, with F1 scores approximately 2% lower and stability reduced by 36.4-60.7% compared to their monolingual (English) counterparts. This decline can be attributed to the need of multilingual models to balance the grammatical and lexical characteristics of different languages during training, increasing the difficulty and complexity, and resulting in poorer performance on single-language tasks compared to specialized monolingual models. The large models with the whole-word masking training strategy show a slightly decreased performance. For instance, the F1-Scores of the LUC-W and LC-W models on the AWARE dataset are 79.5% and 79.34%, which are slightly lower than those of the LUC and LC models without whole-word masking. However, the stability of LUC-W is better than that of the LUC model, with standard deviations of 6.95 \(\times\) 10\({}^{-3}\) and 7.54 \(\times\) 10\({}^{-3}\), respectively. The reason for this improved stability could be that the whole-word masking strategy better captures the overall semantics of words, reducing noise at the subword level and resulting in more consistent performance across multiple trials. However, whole-word masking may also decrease the flexibility in handling fine-grained semantics. From Figure 11, we observe that the uncased models generally maintain high robustness on the BFRU dataset. However, all base models with multilingual features continue to underperform compared to their monolingual versions. Interestingly, several findings differ from the performance observed on the AWARE test set. First, uncased models generally exhibit lower F1 scores than cased versions. While uncased models perform well on familiar data, they slightly lag behind cased models when generalizing to unseen data. This may be because cased models retain case information, allowing them to capture finer semantic distinctions, which gives them an advantage when processing new data.