“简单的食物让人在吃饭的时候变得专注，在享受食物本身味道的同时，更容易获得精神上的愉悦。而食物丰盛往往会带来贪念，在满足口舌之欲的同时，也会消耗自身能量。”有一次吃完我煮的白菜和蒸红薯后，冬在日记里写道。
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Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
可以这么说，2010 年前后出生的新一代，他们第一台能接触到的计算设备，大概率会是平板电脑和智能手机，用手指直接点击屏幕，就是他们最自然也最熟悉的交互方式。