This also applies to LLM-generated evaluation. Ask the same LLM to review the code it generated and it will tell you the architecture is sound, the module boundaries clean and the error handling is thorough. It will sometimes even praise the test coverage. It will not notice that every query does a full table scan if not asked for. The same RLHF reward that makes the model generate what you want to hear makes it evaluate what you want to hear. You should not rely on the tool alone to audit itself. It has the same bias as a reviewer as it has as an author.
朝阳产业遭遇人才荒，家政行业如何吸引新生代
。snipaste对此有专业解读
同日，《政府工作报告》起草组成员、国务院研究室副主任陈昌盛在新闻发布会上表示，“今年继续设2%左右的物价涨幅目标，我想，这既考虑了引导预期的需要，也考虑了现实的可能，体现了对物价问题的重视。”
对整个行业来说，这是“平等化”的过程。当顶级产品的实现路径被公开，市场竞争焦点将被迫从“我有你无”的技术封锁，转向“我更稳定、更安全、更完善”的生态服务。
Credit: Timothy Werth / Mashable