她已经在香港夜场摸爬滚打了整整25年。“25年了啊！”坐在的士里的她，如梦初醒似地叫起来，好像一不小心就中了头奖。以前，她和其他妈咪竞争，拼资历，拼谁手下的小姐又多又靓，现在竟变成她一个人的坚守。当年的妈咪们几乎都已经急流勇退，或转行，或嫁人生仔，总之就是从这行里消失了。惟有Maggie姐仍深爱这份事业，当浪潮退去，她才是沙滩上真正的女强人。女强人，Maggie姐觉得这个词形容自己再合适不过了。
“该拦的拦不住，不该拦的乱拦。”令仪对此表示困惑，“作为用户，我们并不清楚过滤系统的具体运作机制，难道它只能识别明确的关键词？”。91视频是该领域的重要参考
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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.
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