## 5 Experiments
### Experimental Results
The self-correction reflection demonstrates varying degrees of improvement in MSCS across different perturbation types and models as improved alignment between the model's understanding of perturbed inputs and their original meaning, leading to more accurate semantic restoration. As shown in Table 9, for the SwapAlpha perturbation, Llama3.1 shows the most significant semantic correction, with MSCS increasing by +32.68% in MMLU and +27.35% in LSAT, reflecting the model's improved ability to generate corrections semantically closer to the original text. For the CaseAlpha perturbation, most models demonstrate a certain degree of semantic correction, with Llama3.1 again leading to +15.92% improvement in MMLU and +15.14% in LSAT, highlighting the model's partial success in recovering capitalization consistency. In particular, SwapAlpha consistently achieves the highest mean value in MSCS after correction (+10.88%), followed by CaseAlpha (+8.54%) and SwapWord (+1.33%). As shown in Figure 9, the self-correction reflection demonstrates a significant effect in enhancing LLMs' understanding of perturbed questions. All LLMs and datasets exhibit a consistent trend, where the radar chart area increases after applying self-correction reflection, with improvements observed in every radar chart. Notably, the radar chart area expansion is particularly pronounced for Llama3.1 and Qwen2.5, indicating their superior improvement in comprehending perturbed texts after self-correction reflection. These MSCS improvement results validate that self-correction enhances the model's ability to process noisy inputs and produce more accurate outputs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LLMs**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**MSCS of SwapAlpha**} & \multicolumn{3}{c}{**MSCS of SwapWorld**} & \multicolumn{3}{c}{**MSCS of CaseAlpha**} \\  & & & **Before** & **After** & **Improv.** & **Before** & **After** & **Improv.** & **Before** & **After** & **Improv.** \\ \hline \multirow{4}{*}{GLM4} & MMLU & 0.9904 & 0.9936 & +0.32\% & 0.9985 & 0.9986 & +0.01\% & 0.9909 & 0.9939 & +0.30\% \\  & LogiQA-en & 0.9874 & 0.9922 & +0.49\% & 0.9980 & 0.9983 & +0.03\% & 0.9876 & 0.9924 & +0.49\% \\  & LSAT & 0.9466 & 0.9606 & +1.48\% & 0.9888 & 0.9902 & +0.14\% & 0.9483 & 0.9632 & +1.57\% \\  & SAT-en & 0.8243 & 0.8571 & +3.98\% & 0.9048 & 0.9129 & +0.90\% & 0.8331 & 0.8672 & +4.09\% \\ \hline \multirow{4}{*}{Llama3.1} & MMLU & 0.3562 & 0.4726 & +32.68\% & 0.6441 & 0.6741 & +4.66\% & 0.5206 & 0.6035 & +15.92\% \\  & LogiQA-en & 0.3505 & 0.4346 & +23.99\% & 0.6884 & 0.7040 & +2.27\% & 0.5423 & 0.6148 & +13.37\% \\  & LSAT & 0.2962 & 0.3772 & +27.35\% & 0.6652 & 0.6828 & +2.65\% & 0.4781 & 0.5505 & +15.14\% \\  & SAT-en & 0.4209 & 0.4848 & +15.18\% & 0.5604 & 0.5753 & +2.66\% & 0.5787 & 0.6381 & +10.26\% \\ \hline \multirow{4}{*}{Qwen2.5} & MMLU & 0.5597 & 0.6472 & +15.63\% & 0.8288 & 0.8431 & +1.73\% & 0.5753 & 0.6697 & +16.41\% \\  & LogiQA-en & 0.5165 & 0.5946 & +15.12\% & 0.8315 & 0.8420 & +1.26\% & 0.5369 & 0.6308 & +17.49\% \\  & LSAT & 0.4754 & 0.5561 & +16.98\% & 0.8159 & 0.8261 & +1.25\% & 0.4860 & 0.5891 & +21.21\% \\  & SAT-en & 0.6194 & 0.6833 & +10.32\% & 0.8254 & 0.8280 & +0.31\% & 0.6349 & 0.7086 & +11.61\% \\ \hline \multirow{4}{*}{Falcon3-Lambda} & MMLU & 0.7946 & 0.8177 & +2.91\% & 0.8760 & 0.8847 & +0.99\% & 0.8382 & 0.8547 & +1.97\% \\  & LogiQA-en & 0.7981 & 0.8167 & +2.33\% & 0.8806 & 0.8872 & +0.75\% & 0.8415 & 0.8595 & +2.14\% \\ \cline{1-1}  & LSAT & 0.7413 & 0.7605 & +2.59\% & 0.8489 & 0.8561 & +0.85\% & 0.8036 & 0.8214 & +2.22\% \\ \cline{1-1}  & SAT-en & 0.7581 & 0.7788 & +2.73\% & 0.8471 & 0.8535 & +0.76\% & 0.8246 & 0.8441 & +2.36\% \\ \hline \multirow{2}{*}{Avg. Improv.} & - & - & +10.88\% & - & - & +1.33\% & - & - & +8.54\% \\ \hline \hline \end{tabular}
\end{table}