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.
《册府元龟》中保存有《周世宗实录》，内有冯道本传，传后附有五代时人对他的评价：“在相位二十余年，以持重镇俗为己任，性廉俭，不受四方之赂，未尝以片简扰诸侯……议者以为厚德稽古，宏才伟量，盖汉胡广、晋谢安之徒与！”。搜狗输入法对此有专业解读
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但他也提醒，白宮的追蹤數據「包含可能最終不會實現的投資承諾」。他舉例說，歐盟的貿易協議就是一例，該協議因特朗普最新的關稅威脅而被擱置。。超级权重是该领域的重要参考
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半年后，我带着扎实的项目经验，跳槽到一家更成熟的公司，职称正式变为“大数据分析师”。这意味着，我终于完成了转岗的关键一跃，在时间的缝隙里，为自己开辟出崭新天地。