## 5 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LLMs**} & \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**ACC of SwapAlpha**} & \multicolumn{3}{c}{**ACC of SwapWord**} & \multicolumn{3}{c}{**ACC of CaseAlpha**} \\  & & **Before** & **After** & **Improv.** & **Before** & **After** & **Improv.** & **Before** & **After** & **Improv.** \\ \hline \multirow{4}{*}{GLM4} & MMLU & 49.26 & 54.69 & +11.02\% & 47.93 & 49.27 & +2.81\% & 59.15 & 60.72 & +2.64\% \\  & LogQA-en & 36.92 & 40.03 & +8.44\% & 31.31 & 32.40 & +3.48\% & 43.30 & 46.26 & +6.83\% \\  & LSAT & 39.10 & 43.81 & +12.04\% & 31.66 & 32.51 & +2.66\% & 47.03 & 48.27 & +2.63\% \\  & SAT-en & 65.78 & 73.79 & +12.18\% & 54.85 & 56.31 & +2.65\% & 80.58 & 82.04 & +1.81\% \\ \hline \multirow{4}{*}{Llama3.1} & MMLU & 47.29 & 50.85 & +7.51\% & 47.07 & 48.07 & +2.13\% & 53.80 & 55.42 & +3.01\% \\  & LogQA-en & 31.78 & 35.51 & +11.76\% & 28.35 & 29.75 & +4.95\% & 36.99 & 38.47 & +4.00\% \\  & LSAT & 35.48 & 39.45 & +11.17\% & 29.68 & 30.72 & +3.51\% & 40.73 & 43.11 & +5.84\% \\  & SAT-en & 52.67 & 59.71 & +13.36\% & 46.84 & 48.06 & +2.59\% & 61.16 & 64.08 & +4.76\% \\ \hline \multirow{4}{*}{Qwen2.5} & MMLU & 57.19 & 61.80 & +8.06\% & 52.68 & 54.37 & +3.22\% & 65.68 & 66.37 & +1.05\% \\  & LogQA-en & 45.25 & 49.22 & +8.78\% & 38.47 & 39.72 & +3.24\% & 51.40 & 52.49 & +2.12\% \\  & LSAT & 46.33 & 51.34 & +10.80\% & 36.12 & 37.66 & +4.25\% & 53.96 & 54.81 & +1.56\% \\  & SAT-en & 73.54 & 79.61 & +8.25\% & 58.74 & 60.19 & +2.48\% & 82.77 & 84.95 & +2.64\% \\ \hline \multirow{4}{*}{Falcon3-Lambda} & MMLU & 47.24 & 52.10 & +10.30\% & 47.88 & 48.43 & +1.13\% & 56.37 & 57.56 & +2.11\% \\  & LogQA-en & 33.80 & 37.07 & +9.68\% & 36.60 & 37.38 & +2.13\% & 38.47 & 39.56 & +2.83\% \\  & LSAT & 35.28 & 38.35 & +8.71\% & 32.61 & 33.60 & +3.04\% & 39.05 & 40.93 & +4.82\% \\  & SAT-en & 50.97 & 56.80 & +11.43\% & 48.54 & 49.51 & +2.00\% & 59.71 & 62.62 & +4.88\% \\ \hline \multirow{2}{*}{Avg. Improv.} & - & - & +10.22\% & - & - & +2.89\% & - & - & +3.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Response ACC comparison before and after correction for three types of perturbations (SwapAlpha, SwapWord, CaseAlpha) at 100% intensity across four datasets (MMLU, LogQA-en, LSAT, and SAT-en) using four LLMs (GLM4, Llama3.1, Qwen2.5, and Falcon3-Lambda). For each perturbation type, the table presents the ACC values before correction (“Before”), after correction (“After”), and the improvement percentage (“Improv.”). Figure 10: Illustration of ACC enhancement through self-correction reflection, displaying the performance improvement for each LLM across different perturbation types. The radar chart plots normalized ACC percentages (before/after correction relative to 0% perturbation ACC) on axes representing distinct perturbation scenarios. Using self-correction reflection, the models achieved significant improvements not only in semantics but also in stable and reliable performance in practical tasks. This indicates that the approach of self-correction reflection is an effective method for enhancing the performance of LLMs when dealing with input perturbations, offering broad application prospects and research value.