other factors. A proficient evaluation system should not only offer benchmark results but should
also deliver an insightful analysis, recommendations, and guidance for future research and de-
velopment. For instance, PromptBench [249] provides not only robustness evaluation results on
adversarial prompts but also a comprehensive analysis through attention visualization, elucidating
how adversarial texts can result in erroneous responses. The system further offers a word frequency
analysis to identify robust and non-robust words in the test sets, thus providing prompt engineering
guidance for end users. Subsequent research can leverage these findings to enhance LLMs. Another
example is that Wang et al. [203] first explored the performance of large vision-language models
on imbalanced (long-tailed) tasks, which demonstrates the limitation of current large models. Then,
they explored different methodologies to enhance the performance on these tasks. In summary,
ASurveyonEvaluationofLargeLanguageModels