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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.
保持愉快的沟通：尤其是上了幼儿园之后，每天都要找时间与孩子沟通，聊一聊幼儿园的各种事情，学习情况、好玩的玩具、八卦。用来掌握孩子在幼儿园的情况，从一些小事和孩子对事件的反应中，能了解孩子在幼儿园是不是受到欺负或者不公正的待遇，这也是初步跟孩子建立信任的时候，我会当一个合格的倾听者，让孩子愿意跟我交流。
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