Forging Robust Cognition Resilience in Large Language Models: The Self-Correction Reflection Paradigm Against Input Perturbations

Large language models (LLMs) have been widely used in real-world applications due to their ability to decipher and make sense of perturbed information. However, the performance of LLMs that suffered from input perturbations has not been fully analyzed. In this paper, we propose a self-correction reflection method inspired by human cognition to improve robustness and mitigate the impact of perturbations on LLM performance. Firstly, we analyze the vulnerabilities of tokenization in LLMs through empirical demonstrations, comparative analysis with human cognition, and theoretical investigation of perturbation susceptibility. Secondly, we imitate the correction of input information in the human brain, which is enhanced by the consistency principle, aiming to enable the model to correct the perturbation and reduce the impact on the final response. Finally, we conduct experiments to validate the method’s efficacy, demonstrating improved performance across multiple models and datasets. We introduce a new evaluation metric, Model-Specific Cosine Similarity (MSCS), to quantify how well a specific model understands perturbation text, providing a more comprehensive evaluation of different LLM architectures. Particularly for different types of perturbations, after applying self-correction reflection, the average MSCS improvement across all models reaches 10.88%, while the average ACC improvement is 10.22%. The study also underscores the need for more innovative tokenization techniques and architectural design to achieve human-like cognitive robustness. This study could pave the way for more reliable, adaptable, and intelligent language models capable of thriving in practical applications.