As for tasks beyond standard performance, there are benchmarks designed for OOD, adversarial
robustness, and fine-tuning. GLUE-X [221] is a novel attempt to create a unified benchmark
aimed at evaluating the robustness of NLP models in OOD scenarios. This benchmark emphasizes
the significance of robustness in NLP and provides insights into measuring and enhancing the
robustness of models. In addition, Yuan et al. [226] presents BOSS, a benchmark collection for
assessing out-of-distribution robustness in natural language processing tasks. PromptBench [249]
centers on the importance of prompt engineering in fine-tuning LLMs. It provides a standardized
evaluation framework to compare different prompt engineering techniques and assess their impact
on model performance. PromptBench facilitates the enhancement and optimization of fine-tuning
methods for LLMs. To ensure impartial and equitable evaluation, PandaLM [204] is introduced as a
ASurveyonEvaluationofLargeLanguageModels