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.
[&:first-child]:overflow-hidden [&:first-child]:max-h-full"
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* @param low 起始索引
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AI確實存在一些非常現實的問題，從倫理問題到其可能對環境造成的影響。有些人甚至選擇完全不與它互動。但如果你打算使用大型語言模型，那麼學會如何更快、更有效率地獲得你想要的結果，不僅對你有好處，對過程中消耗的能源也可能更有益。以下這些技巧將幫助你入門。