## 6 Discussion  
The experimental results indicate that although LLMs experience performance declines when confronted with various perturbations, the introduction of the self-correction reflection method significantly enhances the models' capabilities, demonstrating improved robustness and accuracy. In particular, for the SwapAlpha perturbation, after applying self-correction reflection, the average MSCS improvement across all models reaches 10.88%, while the average ACC improvement is 10.22%.  
The self-correction reflection method proposed in this study provides a promising approach to enhance the robustness of the response. By simulating the brain's sequential steps to correct input perturbations, this mechanism effectively mitigates the reduction in accuracy caused by input perturbations. This improvement is consistent with neurological and psychological research on input perturbations. Experimental results demonstrated that integrating self-correction reflection can significantly improve model performance under input perturbation conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**LLMs**} & \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**GPU Usage (GB)**} & \multicolumn{3}{c}{**Extra Overhead (\%)**} \\  & & **w/o HECP** & **Ours** & & \\ \hline \multirow{4}{*}{GLM4} & MMLU & \multirow{4}{*}{18.55} & \multirow{4}{*}{19.32} & \multirow{4}{*}{4.15} \\  & LogiQA-en & LSAT & & & & \\  & SAT-en & & & & \\ \hline \multirow{4}{*}{Llama3.1} & MMLU & \multirow{4}{*}{16.34} & \multirow{4}{*}{17.37} & \multirow{4}{*}{6.30} \\  & LogiQA-en & LSAT & & & \\  & SAT-en & & & & \\ \hline \multirow{4}{*}{Qwen2.5} & MMLU & \multirow{4}{*}{16.67} & \multirow{4}{*}{17.86} & \multirow{4}{*}{7.14} \\  & LogiQA-en & LSAT & & & \\ \cline{1-1}  & SAT-en & & & & \\ \hline \multirow{4}{*}{Falcon3-Mamba} & MMLU & \multirow{4}{*}{16.84} & \multirow{4}{*}{18.04} & \multirow{4}{*}{7.13} \\ \cline{1-1}  & LogiQA-en & LSAT & & & \\ \cline{1-1}  & SAT-en & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Response GPU memory usage (in GB) of the self-correction reflection (Ours) with the ablated variant (w/o HECP) evaluated on four datasets using four LLMs.  
We did not undertake generation tasks in our evaluation of LLMs because assessing the output quality can often be challenging. Large language models are capable of generating highly creative or high-quality text, which makes evaluation difficult without reference outputs. According to [45], these challenges can impair effective assessment. Therefore, we opted for classification tasks with clear answers for our tests. Future work may include optimizing these evaluation methods.  
Despite these advances, our results also highlight the limitations of current approaches. While self-correction reflection improves the model's ability to handle input perturbations, it also increases energy consumption due to the implementation of the consistency principle, which expands the context input. The additional context grows with the size of the user's input text, leading to extra memory or GPU memory overhead for data processing. However, these additional overheads are negligible (average +6.18% across models, or an absolute increase of 0.77-1.2 GB) compared to the substantial memory and GPU memory consumption primarily caused by the huge number of parameters of LLMs during operation (16.34-18.55 GB).