results of this study emphasize the potential risks to the overall system security when manipulating
visual input. For vision-language models, Zhao et al. [244] evaluated LLMs on visual input and
transferred them to other visual-linguistic models, revealing the vulnerability of visual input. Li
et al. [104] provided an overview of OOD evaluation for language models: adversarial robustness,
domain generalization, and dataset biases. Bridging these lines of research, the authors conducted a
comparative analysis, unifying the three approaches. They succinctly outlined the data-generation
processes and evaluation protocols for each line of study, all while emphasizing the prevailing
ASurveyonEvaluationofLargeLanguageModels