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.
「在我被抓之前，我在美國生活的這幾年，我就是很低調、默默獨自生活，很多時候遇到有問題我都是自己面對、自己解決，我都盡量不去求別人來幫我。」
。WPS下载最新地址是该领域的重要参考
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And check the next blog post about a simplified deprecation API built on top of BPatterns: