Photograph: Julian Chokkattu
In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
，这一点在WPS官方版本下载中也有详细论述
那些炊帚都是高粱苗用铁丝绑成的。因为使用得久，有苗的一边已经被磨秃了，所以才叫它炊帚疙瘩。那种炊帚其实家家灶台上都有，刷锅用的。可孩子们更愿意到外面去“捡”。家里的东西再烂也是宝贝，谁都不会舍得往外拿。
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