Abstract: With the rapid development of large language models, their broad application prospects have attracted significant attention from both the academic and industrial communities.Before a large language model is applied to practice,its performance and potential risks need to be comprehensively evaluated.In recent years,the evaluation methods of large language models have been studied from multiple perspectives by researchers.In this paper，the evaluation metrics,methods and benchmarks of large language models in terms of performance,robustness,and alignment,are reviewed systematically and the advantages and disadvantages of various evaluation metrics and methods are analyzed.Finally,the future research directions and challenges of large language model evaluation are discussed.

Key words: Large language models, Evaluation methods, Evaluation metrics, Evaluation benchmarks