  In current benchmarks for evaluating large language models (LLMs), there are
issues such as evaluation content restriction, untimely updates, and lack of
optimization guidance. In this paper, we propose a new paradigm for the
measurement of LLMs: Benchmarking-Evaluation-Assessment. Our paradigm shifts
the "location" of LLM evaluation from the "examination room" to the "hospital".
Through conducting a "physical examination" on LLMs, it utilizes specific
task-solving as the evaluation content, performs deep attribution of existing
problems within LLMs, and provides recommendation for optimization.
