LLMs have gotten attention across various research domains due to their
exceptional performance on a wide range of complex tasks. Therefore, refined
methods to evaluate the capabilities of LLMs are needed to determine the tasks
and responsibility they should undertake. Our study mainly discussed how LLMs,
as useful tools, should be effectively assessed. We proposed the two-stage
framework: from ``core ability'' to ``agent'', clearly explaining how LLMs can
be applied based on their specific capabilities, along with the evaluation
methods in each stage. Core ability refers to the capabilities that LLMs need
in order to generate high-quality natural language texts. After confirming LLMs
possess core ability, they can solve real-world and complex tasks as agent. In
the "core ability" stage, we discussed the reasoning ability, societal impact,
and domain knowledge of LLMs. In the ``agent'' stage, we demonstrated embodied
action, planning, and tool learning of LLMs agent applications. Finally, we
examined the challenges currently confronting the evaluation methods for LLMs,
as well as the directions for future development.

通过讨论 LLMs 的核心能力、评估方法以及应用领域，我们提出了一个两阶段的框架来有效评估 LLMs 的能力，并考察了当前评估方法所面临的挑战和未来发展方向。