## 5 Experiment Results and Analysis
### Experiment for Generalization in Low-Resource Scenarios (Answer to RQ3)  
A set of ablation experiments is designed to comprehensively assess the generalization capability of the LePB-SA4 RE. The entire AWARE dataset is used for training, and the training set size is gradually reduced from 90% to 10%. The BFRU and Dabrowski datasets are served as generalization validation. Figure 5 illustrates the performance variation curves of LePB-SA4 RE and the SOTA models, with each data point representing the average of ten results across different training scale settings. To verify the statistical significance and reliability of the experimental results [47; 48; 49], the SD of the curves and the CI at a 99% confidence level are calculated, as shown in Table 6 and Figure 6.  
Due to the randomness in the distribution of training and validation sets during the tuning process, all results in Figure 5 represent the average values of ten parallel tests to minimize errors. At the 90% training set ratio, LePB-SA4 RE outperforms SOTA in both accuracy and F1-Score. With training set sizes greater than 50%, the proposed model demonstrates better stability, with the fluctuations of the four red curves (high-contrast curves) being smaller than those of the FT method. On the _BFRU_ test dataset (eight times larger than _Dabrowski_), the SOTA model exhibits a significant performance drop when the training set size is reduced from 30% to 10%. The final average loss of LePB-SA4 RE is 5% lower than that of the traditional method. In contrast, the proposed method shows slight improvements after a minor decline, underscoring the advantage of the FT and lexicon-enhanced hybrid approach in low-resource scenarios. While the performance of both models aligns during the last 30% on the Dabrowski dataset, LePB-SA4 RE still experiences a lower rate of decline compared to the traditional method. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Components** & **Templates** & **Round 1** & **Round 2** & **Round 3** & **Round 4** & **Round 5** & **Average** \\ \hline - & \(N/A\) (_Fine-tuning_) & 73.58 & 75.23 & 77.25 & 76.64 & 74.11 & 74.96 \\ - & \([MASK]\) & 72.49 & 71.90 & 73.17 & 72.00 & 70.96 & 72.10 \\ \hline \multirow{3}{*}{Single} & \(R+[MASK]\) & 72.32 & 73.29 & 73.82 & 74.36 & 70.72 & 72.90 \\  & \(O+[MASK]\) & 72.51 & 73.92 & 73.93 & 69.31 & 73.53 & 72.64 \\  & \(T+[MASK]\) & **73.61** & **72.43** & **74.04** & **75.89** & **74.52** & **74.10** \\ \hline \multirow{3}{*}{Binary} & \(f(R,O)+[MASK]\) & 74.45 & 75.00 & 71.15 & 75.00 & 76.49 & 74.42 \\  & \(f(R,T)+[MASK]\) & **72.90** & **74.67** & **76.54** & **75.74** & **75.32** & **75.03** \\  & \(f(T,O)+[MASK]\) & 75.50 & 72.64 & 76.49 & 74.56 & 74.31 & 74.70 \\ \hline \multirow{3}{*}{Triple} & \(R+f(T,O)+[MASK]\) & 76.61 & 75.59 & 76.61 & 75.66 & 75.35 & 75.96 \\  & \(R+f(T,O)+[MASK]+QA\) & **76.84** & **77.11** & **76.69** & **76.37** & **76.67** & **76.74** \\ \hline \multirow{3}{*}{Lexicon-based modulation layer + Single \((T)\)} & 75.31 & 76.15 & 73.82 & 72.66 & 74.97 & 74.58 \\ \cline{1-1}  & Lexicon-based modulation layer + Binary \((R,T)\) & 76.55 & 75.91 & 76.50 & 74.90 & 74.89 & 75.75 \\ \cline{1-1}  & Lexicon-based modulation layer + Triple \((QA)\) & **79.39** & **77.64** & **78.84** & **77.89** & **78.76** & **78.50** \\ \hline \hline \end{tabular}  
* The bold means high-performance results.  
\end{table} 
Table 5: The impact of prompt templates and modulation layer. Figure 5: Performance variation curves on other datasets. Figure 6: Confidence interval at 99% critical value. \begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**BFRU**} & \multicolumn{2}{c}{**Dabrowski**} \\ \cline{2-5} \cline{2-5}  & **SD\_Acc** & **SD\_F1** & **SD\_Acc** & **SD\_F1** \\ \hline
**SOTA** & 0.6528 & 0.7315 & 0.5307 & 0.4835 \\
**LePB-SA4 RE** & **0.2847** & **0.2048** & **0.4578** & **0.3406** \\ \hline \end{tabular} The bold means high-performance results.  
\end{table} 
Table 6: Standard deviation of the performance fluctuation curves.