Statistics of what works best
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.，详情可参考体育直播
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这份过于私人的自传式表达遭遇苏联业内一片质疑和不满，成为他创作生涯中一次激烈的争议。老搭档尤索夫拒绝执镜，认为这部作品过于自我；与他一同撰写《安德烈・卢布廖夫》剧本的旧友冈察洛夫斯基在看过影片后直言：“塔可夫斯基关心的并不是永恒的真理，而完全是他自己。”塔可夫斯基从未妥协，正如他在日记中所写的，电影于他从来不是一份谋生的职业，而是“与上帝的对话”，是内心的祈祷。这份对创作的执念同样体现在《潜行者》的拍摄中，也让他付出了沉重的代价。