## 5 Experiments
### Experimental Setups  
#### 5.1.1 Models and Datasets  
We selected a diverse set of models representative of state-of-the-art architectures commonly used in LLM research tasks. Specifically, we included models based on the Transformer architecture, such as Qwen2.5 (7B parameters) [28; 29] (Alibaba, Hangzhou, China), GLM4 (9B parameters) [30] (Tsinghua University, Beijing, China), and Llama3.1 (8B parameters) [31] (Meta AI, Menlo Park, CA, USA), as well as models with the Mamba architecture, including Falcon3-Mamba (7B parameters) [32] (Technology Innovation Institute, Abu Dhabi, UAE). These models were chosen for their widespread adoption and proven efficacy in various tasks [33; 34]. In this study, we selected two well-established benchmark datasets commonly used for evaluating LLMs: the Massive Multitask Language Understanding (MMLU) dataset [35] (the MMLU datasets can be downloaded from the following link: [[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/))hendrycks/test, accessed on 11 November 2024) and the AGIEval dataset [36] (the AGIEval datasets can be downloaded from the following link: [[https://github.com/ruxiangcui/AGIEval](https://github.com/ruxiangcui/AGIEval)]([https://github.com/ruxiangcui/AGIEval](https://github.com/ruxiangcui/AGIEval)), accessed on 15 November 2024), with a focus on the English subsets. The MMLU dataset is a widely recognized benchmark covering a broad range of tasks [37], including, but not limited to, single-choice questions in domains such as science, history, and mathematics. Similarly, the AGIEval dataset is a collection of question-answering tasks, comprising multiple sub-datasets, specifically LogiQA dataset [38], LSAT dataset [39], and SAT dataset. These datasets, consisting of single-choice questions and the corresponding answers, are designed to test the reasoning and problem-solving abilities of the models, making them ideal for assessing the generalizability of LLM [8; 40]. We applied a series of controlled perturbations to the datasets, following the methodologies proposed by previous studies [7], where four different perturbation levels were used to explore the performance of the LLMs under varying degrees of input noise. The first level was No Perturbation (Baseline), where no perturbation was applied to the dataset, serving as the control condition for model evaluation. In the second level, Light Perturbation, 20% of the data points that met the specific perturbation criteria were randomly altered, introducing minor noise while preserving the original meaning. The third level, Moderate Perturbation, affected 50% of the data points that satisfied the perturbation conditions, introducing more substantial noise without completely disrupting the underlying information. The fourth level, Heavy Perturbation, applied changes to 100% of the data points, ensuring that all data underwent some form of noise introduction, simulating high levels of disturbance. Furthermore, a case perturbation was applied based on research on human sensitivity to the letter case [41; 42]. For each data point, we randomly selected 0%, 20%, 50%, or 100% of the words and swapped their case. This perturbation is regarded as real-world variations in text input with case inconsistencies, testing the model's robustness to such a swap. Based on research on human sensitivity to letter and word order [43], we introduce additional letter and word perturbations. For letter perturbation, we randomly selected 0%, 20%, 50%, or 100% of words and swapped the order of two adjacent letters within those words, simulating typing or OCR errors. For word perturbation, we selected 0%, 20%, 50%, or 100% of adjacent words and swapped their order, mimicking sentence-level disorganization often encountered in user-generated content with non-native speakers.