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.
Lauren Caulfield, whose daughter was stillborn in March 2022, said they have "fought" for this review.。关于这个话题，PG官网提供了深入分析
自“长江首城”出发，我们试图通过五个核心问题，拆解宜宾如何“逐立上游”，向“AI新城”跃迁的底层逻辑。，详情可参考谷歌
Linux with GTK and GNOME