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.
回到意大利之後，安迪把「蛋炒飯」作為一項重要技能引進介紹給身邊的朋友，認為「把早餐的面包替換成這個能對自己稍微好點」。
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"We like time to free up our mind. I get the best ideas when I walk my dog," says Marieke Pepers, chief people officer at the Dutch software firm Nmbrs.
NYT Strands spangram answer todayToday's spangram is Glamorous.