## 5 Experiments
### Experimental Results  
The experiment evaluated the performance of self-correction reflection by measuring the semantic divergence between the perturbed and original texts. We quantified this divergence using the mean semantic cosine similarity (MSCS) calculated with each model and its tokenizer, where a lower MSCS indicates stronger input perturbations from the model's perspective. Additionally, we adopted accuracy (ACC) to assess the impact of perturbations and the improvement after self-correction. As shown in Table 4, the mean semantic cosine similarity (Equation (11)) consistently decreases as perturbation levels increase across all evaluated language models. This demonstrates that more severe perturbations lead to greater semantic divergence between the modified text and the original version, confirming the MSCS metric's effectiveness in quantifying semantic degradation. For unmodified texts (0% perturbation), all models maintain MSCS scores of 1.00, indicating no measurable semantic deviation from the original texts. This baseline confirms that the observed decreases in similarity scores directly result from the perturbations introduced rather than any inherent model limitations. The consistent inverse relationship between perturbation intensity and MSCS scores across all models and datasets demonstrates how textual modifications progressively degrade semantic fidelity. These findings highlight both the sensitivity of current language models to various types of perturbations and the utility of MSCS as a robust metric for quantifying these effects. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LMs**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**MSCS**} & \multicolumn{2}{c}{**MSCS of SwapAlpha**} & \multicolumn{2}{c}{**MSCS of SwapWord**} & \multicolumn{2}{c}{**MSCS of CaseAlpha**} \\  & & **0\%** & **20\%** & **50\%** & **100\%** & **20\%** & **50\%** & **100\%** & **20\%** & **50\%** & **100\%** \\ \hline \multirow{4}{*}{GLM4} & MMLU & 1.00 & 0.9985 & 0.9960 & 0.9904 & 0.9990 & 0.9988 & 0.9985 & 0.9986 & 0.9963 & 0.9909 \\  & LogQA-en & 1.00 & 0.9984 & 0.9955 & 0.9874 & 0.9984 & 0.9982 & 0.9980 & 0.9985 & 0.9985 & 0.9876 \\  & LSAT & 1.00 & 0.9888 & 0.9736 & 0.9466 & 0.9942 & 0.9919 & 0.9888 & 0.9908 & 0.9761 & 0.9483 \\  & SAT-en & 1.00 & 0.9506 & 0.8956 & 0.8243 & 0.9368 & 0.9219 & 0.9048 & 0.9625 & 0.9084 & 0.8331 \\ \hline \multirow{4}{*}{Llama3.1} & MMLU & 1.00 & 0.8442 & 0.6090 & 0.3562 & 0.8051 & 0.7043 & 0.6441 & 0.8505 & 0.6924 & 0.5206 \\  & LogQA-en & 1.00 & 0.7786 & 0.5475 & 0.3505 & 0.7914 & 0.7263 & 0.6884 & 0.8317 & 0.6952 & 0.5423 \\  & LSAT & 1.00 & 0.7067 & 0.4880 & 0.2962 & 0.7751 & 0.7104 & 0.6652 & 0.7635 & 0.6296 & 0.4781 \\  & SAT-en & 1.00 & 0.7260 & 0.5670 & 0.4209 & 0.6426 & 0.5967 & 0.5604 & 0.8046 & 0.7007 & 0.5787 \\ \hline \multirow{4}{*}{Qwen2.5} & MMLU & 1.00 & 0.8885 & 0.7364 & 0.5597 & 0.9007 & 0.8557 & 0.8288 & 0.8917 & 0.7596 & 0.5753 \\  & LogQA-en & 1.00 & 0.8284 & 0.6826 & 0.5165 & 0.8906 & 0.8565 & 0.8315 & 0.8629 & 0.7280 & 0.5369 \\  & LSAT & 1.00 & 0.7915 & 0.6436 & 0.4754 & 0.8770 & 0.8410 & 0.8159 & 0.8293 & 0.6914 & 0.4860 \\  & SAT-en & 1.00 & 0.8492 & 0.7488 & 0.6194 & 0.8435 & 0.8352 & 0.8254 & 0.8758 & 0.7803 & 0.6349 \\ \hline \multirow{4}{*}{Falcon3-Lambda} & MMLU & 1.00 & 0.9168 & 0.8462 & 0.7946 & 0.9253 & 0.8940 & 0.8760 & 0.9236 & 0.8746 & 0.8382 \\  & LogQA-en & 1.00 & 0.8988 & 0.8411 & 0.7981 & 0.9215 & 0.8960 & 0.8806 & 0.9209 & 0.8804 & 0.8415 \\ \cline{1-1}  & LSAT & 1.00 & 0.8482 & 0.7869 & 0.7413 & 0.8962 & 0.8683 & 0.8489 & 0.8808 & 0.8403 & 0.8036 \\ \cline{1-1}  & SAT-en & 1.00 & 0.8633 & 0.8060 & 0.7581 & 0.8866 & 0.8631 & 0.8471 & 0.9103 & 0.8674 & 0.8246 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Response MSCS among different perturbations (SwapAlpha, SwapWord, CaseAlpha) of different levels (0%, 20%, 50%, 100%) across four datasets (MMLU, LogiQA-en, LSAT, and SAT-en) using four LLMs (GLM4, Llama3.1, Qwen2.5, and Falcon3-Lambda).