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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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Grammarly has a more friendly UI/UX whereas the ProWritingAid interface is not friendly as Grammarly.

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這個結論本身就有些令人不安。當公司設計像ChatGPT或谷歌的Gemini這樣的AI時,會使其行為像人一樣,所以它們有時看起來好像有情緒,你可以控制它們,或者可以引導它們的個性,這也就不足為奇了。但不要被其所迷惑。AI工具只是模仿者,不是生命體。它們只是在模擬人類行為。如果你想要更好的答案,就不要把AI當人看待,而應該把它當成工具來使用。

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